From f6ec9efeae4c0fc5580f9748b12cf068730ef510 Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Tue, 21 Apr 2026 18:00:14 +0200
Subject: [PATCH 1/2] Display code snippets

---
 docs/index.html          | 370 ++++++++++++++++++++++++++++++---------
 docs/javascript.html     | 205 ++++++++++++++++++++++
 docs/problems.html       | 165 ++++++++---------
 docs/table_styles.css    |  97 ++++++++++
 docs/table_template.html |   9 +
 yaml_to_html.py          |  38 +++-
 6 files changed, 727 insertions(+), 157 deletions(-)

diff --git a/docs/index.html b/docs/index.html
index 9b4cad3..78f6a70 100644
--- a/docs/index.html
+++ b/docs/index.html
@@ -55,7 +55,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
     </tr>
   </thead>
   <tbody>
-    <tr>
+    <tr data-problem-id="bbob">
       <td>BBOB</td>
       <td></td>
       <td>suite</td>
@@ -70,7 +70,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj">
       <td>BBOB-biobj</td>
       <td></td>
       <td>suite</td>
@@ -85,7 +85,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_noisy">
       <td>BBOB-noisy</td>
       <td></td>
       <td>suite</td>
@@ -100,7 +100,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
       <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_largescale">
       <td>BBOB-largescale</td>
       <td></td>
       <td>suite</td>
@@ -115,7 +115,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_mixint">
       <td>BBOB-mixint</td>
       <td></td>
       <td>suite</td>
@@ -130,7 +130,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj_mixint">
       <td>BBOB-biobj-mixint</td>
       <td></td>
       <td>suite</td>
@@ -145,7 +145,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_constrained">
       <td>BBOB-constrained</td>
       <td></td>
       <td>suite</td>
@@ -160,7 +160,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="morepo">
       <td>MOrepo</td>
       <td></td>
       <td>suite</td>
@@ -175,7 +175,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="zdt">
       <td>ZDT</td>
       <td></td>
       <td>suite</td>
@@ -190,7 +190,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
       <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dtlz">
       <td>DTLZ</td>
       <td></td>
       <td>suite</td>
@@ -205,7 +205,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
       <td><a href="https://pymoo.org/problems/many/dtlz.html">https://pymoo.org/problems/many/dtlz.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="wfg">
       <td>WFG</td>
       <td></td>
       <td>suite</td>
@@ -220,7 +220,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
       <td><a href="https://pymoo.org/problems/many/wfg.html">https://pymoo.org/problems/many/wfg.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cdmp">
       <td>CDMP</td>
       <td></td>
       <td>suite</td>
@@ -235,7 +235,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sdp">
       <td>SDP</td>
       <td></td>
       <td>suite</td>
@@ -250,7 +250,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="maop">
       <td>MaOP</td>
       <td></td>
       <td>suite</td>
@@ -265,7 +265,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bp">
       <td>BP</td>
       <td></td>
       <td>suite</td>
@@ -280,7 +280,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="gpd">
       <td>GPD</td>
       <td></td>
       <td>generator</td>
@@ -295,7 +295,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="etmof">
       <td>ETMOF</td>
       <td></td>
       <td>suite</td>
@@ -310,7 +310,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
       <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mmopp">
       <td>MMOPP</td>
       <td></td>
       <td>suite</td>
@@ -325,7 +325,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
       <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cfd">
       <td>CFD</td>
       <td>expensive evaluations 30s-15m</td>
       <td>suite</td>
@@ -340,7 +340,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
       <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gbea">
       <td>GBEA</td>
       <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
       <td>suite</td>
@@ -355,7 +355,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
       <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="car_structure">
       <td>Car structure</td>
       <td>54 constraints</td>
       <td>suite</td>
@@ -370,7 +370,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
       <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="emo2017">
       <td>EMO2017</td>
       <td></td>
       <td>suite</td>
@@ -385,7 +385,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="jsec2019">
       <td>JSEC2019</td>
       <td>expensive evaluations 3s; 22 constraints</td>
       <td>single</td>
@@ -400,7 +400,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="re">
       <td>RE</td>
       <td></td>
       <td>suite</td>
@@ -415,7 +415,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cre">
       <td>CRE</td>
       <td></td>
       <td>suite</td>
@@ -430,7 +430,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="radar_waveform">
       <td>Radar waveform</td>
       <td></td>
       <td>single</td>
@@ -445,7 +445,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
       <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mf2">
       <td>MF2</td>
       <td></td>
       <td>suite</td>
@@ -460,7 +460,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
       <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="amvop">
       <td>AMVOP</td>
       <td></td>
       <td>suite</td>
@@ -475,7 +475,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="rwmvop">
       <td>RWMVOP</td>
       <td></td>
       <td>suite</td>
@@ -490,7 +490,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sbox_cost">
       <td>SBOX-COST</td>
       <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
       <td>suite</td>
@@ -505,7 +505,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/">https://github.com/IOHprofiler/IOHexperimenter/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mnk_landscapes">
       <td>ρMNK-Landscapes</td>
       <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
       <td>generator</td>
@@ -520,7 +520,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mubqp">
       <td>mUBQP</td>
       <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
       <td>generator</td>
@@ -535,7 +535,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mtsp">
       <td>ρmTSP</td>
       <td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
       <td>generator</td>
@@ -550,7 +550,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2015_dmoo">
       <td>CEC2015-DMOO</td>
       <td></td>
       <td>suite</td>
@@ -565,7 +565,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ealain">
       <td>Ealain</td>
       <td>Real-world-like, easily extensible to increase complexity</td>
       <td>generator</td>
@@ -580,7 +580,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
       <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ma_bbob">
       <td>MA-BBOB</td>
       <td>Generator that creates affine combinations of BBOB functions</td>
       <td>generator</td>
@@ -595,7 +595,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mpm2">
       <td>MPM2</td>
       <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
       <td>generator</td>
@@ -610,7 +610,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
       <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="convex_dtlz2">
       <td>Convex DTLZ2</td>
       <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
       <td>single</td>
@@ -625,7 +625,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inverted_dtlz1">
       <td>Inverted DTLZ1</td>
       <td>Variant of DTLZ1 with an inverted Pareto front</td>
       <td>single</td>
@@ -640,7 +640,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_dtlz">
       <td>Minus DTLZ</td>
       <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
       <td>suite</td>
@@ -655,7 +655,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_wfg">
       <td>Minus WFG</td>
       <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
       <td>suite</td>
@@ -670,7 +670,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l1_zdt">
       <td>L1-ZDT</td>
       <td>Variant of ZDT with linkages between variables within one of two groups but not between variables in a different group; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -685,7 +685,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_zdt">
       <td>L2-ZDT</td>
       <td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -700,7 +700,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_zdt">
       <td>L3-ZDT</td>
       <td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -715,7 +715,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_dtlz">
       <td>L2-DTLZ</td>
       <td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -730,7 +730,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_dtlz">
       <td>L3-DTLZ</td>
       <td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -745,7 +745,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2018_dt_cec2018_competition_on_dynamic_multiobjective_optimisation">
       <td>CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
       <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
       <td>suite</td>
@@ -760,7 +760,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
       <td><a href="https://pymoo.org/problems/dynamic/df.html">https://pymoo.org/problems/dynamic/df.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="modact_multiobjective_design_of_actuators">
       <td>MODAct - multiobjective design of actuators</td>
       <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> package installed; evaluation times around 20ms</td>
       <td>suite</td>
@@ -775,7 +775,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
       <td><a href="https://pymoo.org/problems/constrained/modact.html">https://pymoo.org/problems/constrained/modact.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="iohclustering">
       <td>IOHClustering</td>
       <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data, with the number of clusters defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets</td>
       <td>suite; generator</td>
@@ -790,7 +790,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg_ii">
       <td>GNBG-II</td>
       <td>Generalized Numerical Benchmark Generator (version 2). Also in IOH <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
       <td>suite; generator</td>
@@ -805,7 +805,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
       <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg">
       <td>GNBG</td>
       <td>Generalized Numerical Benchmark Generator</td>
       <td>suite; generator</td>
@@ -820,7 +820,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
       <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dynamicbinval">
       <td>DynamicBinVal</td>
       <td>Four versions of the dynamic binary value problem</td>
       <td>suite</td>
@@ -835,7 +835,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="pbo">
       <td>PBO</td>
       <td>Suite of 25 binary optimization problems</td>
       <td>suite</td>
@@ -850,7 +850,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="w_model">
       <td>W-model</td>
       <td>Tunable generator for binary optimization based on several difficulty features</td>
       <td>generator</td>
@@ -865,7 +865,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
       <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="submodular_optimitzation">
       <td>Submodular Optimitzation</td>
       <td>set of graph-based submodular optimization problems from 4 problem types</td>
       <td>suite</td>
@@ -880,7 +880,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2013">
       <td>CEC2013</td>
       <td>suite used for cec2013 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -895,7 +895,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2022">
       <td>CEC2022</td>
       <td>suite used for cec2022 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -910,7 +910,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_zeromaxsphere">
       <td>Onemax+Sphere / Zeromax+Sphere</td>
       <td></td>
       <td>single</td>
@@ -925,7 +925,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_deceptivetraprotatedellipsoid">
       <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -940,7 +940,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inversedeceptivetraprotatedellipsoid_deceptivetraprotatedellipsoid">
       <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -955,7 +955,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="porkchopplotinterplanetarytrajectory">
       <td>PorkchopPlotInterplanetaryTrajectory</td>
       <td></td>
       <td>suite</td>
@@ -970,7 +970,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="kinematicsrobotarm">
       <td>KinematicsRobotArm</td>
       <td></td>
       <td>suite</td>
@@ -985,7 +985,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="vehicledynamics">
       <td>VehicleDynamics</td>
       <td></td>
       <td>suite</td>
@@ -1000,7 +1000,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
       <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mechbench">
       <td>MECHBench</td>
       <td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
       <td>Problem Suite</td>
@@ -1015,7 +1015,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
       <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="expobench">
       <td>EXPObench</td>
       <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
       <td>Problem Suite</td>
@@ -1030,7 +1030,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
       <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gasoline_direct_injection_engine_design">
       <td>Gasoline direct injection engine design</td>
       <td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
       <td>Single Problem</td>
@@ -1045,7 +1045,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="beacon">
       <td>BEACON</td>
       <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
       <td>Generator</td>
@@ -1060,7 +1060,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
       <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="tulipaenergy">
       <td>TulipaEnergy</td>
       <td>Determine the optimal investment and operation decisions  for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
       <td>Problem Suite</td>
@@ -1075,7 +1075,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td>See <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
       <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ato">
       <td>ATO</td>
       <td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
       <td>Single Problem</td>
@@ -1090,7 +1090,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td>-</td>
     </tr>
-    <tr>
+    <tr data-problem-id="brachytherapy_treatment_planning">
       <td>Brachytherapy treatment planning</td>
       <td>Treatment planning for internal radiation therapy</td>
       <td>Problem Suite</td>
@@ -1105,7 +1105,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="fleetopt">
       <td>FleetOpt</td>
       <td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Single Problem</td>
@@ -1120,7 +1120,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Not public: was done for real client with their private data</td>
     </tr>
-    <tr>
+    <tr data-problem-id="building_spatial_design">
       <td>Building spatial design</td>
       <td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
       <td>Single Problem</td>
@@ -1135,7 +1135,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
       <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="electric_motor_design_optimization">
       <td>Electric Motor Design Optimization</td>
       <td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
       <td>Single Problem</td>
@@ -1150,7 +1150,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a> (paper in Slovene)</td>
       <td>Implementation not freely available</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bono_bench">
       <td>BONO-Bench</td>
       <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties (different types of multimodality and challenges in decision and objective space) as well as Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
       <td>Generator</td>
@@ -1165,7 +1165,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="randoptgen">
       <td>RandOptGen</td>
       <td>RandOptGen: A Unified Random Problem Generator for Single-and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces</td>
       <td>Generator</td>
@@ -1180,7 +1180,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td></td>
       <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cuter">
       <td>CUTEr</td>
       <td>A constrained and unconstrained testing environment</td>
       <td>Problem Suite</td>
@@ -1195,7 +1195,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
       <td>Not Found</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cutest">
       <td>CUTEst</td>
       <td>The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software</td>
       <td>Problem Suite</td>
@@ -1210,7 +1210,7 @@ <h2>OPL &ndash; Optimisation problem library</h2>
       <td><a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
       <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="puboi">
       <td>PUBOi</td>
       <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
       <td>Generator</td>
@@ -1234,10 +1234,24 @@ <h2>OPL &ndash; Optimisation problem library</h2>
     <h3 class="details-title">Problem details</h3>
     <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
     <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
 </section>
 
 
 <script>
+const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/code_snippets/code_snippets';
+const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/code_snippets/code_snippets';
+const snippetCache = new Map();
+let markdownParserPromise = null;
+
 const table = new DataTable('#problems', {
     layout: {
         topStart: 'search',
@@ -1279,6 +1293,12 @@ <h3 class="details-title">Problem details</h3>
 
 const detailsHint = document.getElementById('problem-details-hint');
 const detailsContent = document.getElementById('problem-details-content');
+const snippetPath = document.getElementById('problem-snippet-path');
+const snippetHint = document.getElementById('problem-snippet-hint');
+const snippetContent = document.getElementById('problem-snippet-content');
+const snippetFolderLink = document.getElementById('problem-snippet-folder-link');
+const readmeHint = document.getElementById('problem-readme-hint');
+const readmeContent = document.getElementById('problem-readme-content');
 
 const stripHtml = (value) => {
     const wrapper = document.createElement('div');
@@ -1292,6 +1312,196 @@ <h3 class="details-title">Problem details</h3>
     return wrapper.innerHTML;
 };
 
+const ensureMarkdownParser = () => {
+    if (window.marked?.parse) {
+        return Promise.resolve(window.marked.parse);
+    }
+
+    if (markdownParserPromise) {
+        return markdownParserPromise;
+    }
+
+    markdownParserPromise = new Promise((resolve, reject) => {
+        const script = document.createElement('script');
+        script.src = 'https://cdn.jsdelivr.net/npm/marked/marked.min.js';
+        script.async = true;
+        script.onload = () => {
+            if (window.marked?.parse) {
+                resolve(window.marked.parse);
+            } else {
+                reject(new Error('Marked loaded without parse function'));
+            }
+        };
+        script.onerror = () => reject(new Error('Failed to load marked library'));
+        document.head.appendChild(script);
+    });
+
+    return markdownParserPromise;
+};
+
+const renderMarkdownToHtml = async (markdownText) => {
+    if (!markdownText) {
+        return '';
+    }
+
+    try {
+        const parse = await ensureMarkdownParser();
+        return parse(markdownText);
+    } catch (error) {
+        return `<pre><code>${escapeHtml(markdownText)}</code></pre>`;
+    }
+};
+
+const toProblemId = (value) => {
+    if (!value) {
+        return '';
+    }
+
+    return value
+        .toLowerCase()
+        .trim()
+        .replace(/[-\s]+/g, '_')
+        .replace(/[^a-z0-9_]/g, '')
+        .replace(/_+/g, '_')
+        .replace(/^_+|_+$/g, '');
+};
+
+const setSnippetState = ({
+    path = '',
+    hint = '',
+    code = '',
+    folderHref = '',
+    readmeHintText = '',
+    readmeHtml = ''
+}) => {
+    if (snippetPath) {
+        snippetPath.textContent = path || 'call_{problem_id}.py';
+    }
+
+    if (snippetHint) {
+        snippetHint.textContent = hint;
+    }
+
+    if (snippetContent) {
+        snippetContent.textContent = code;
+    }
+
+    if (snippetFolderLink) {
+        if (folderHref) {
+            snippetFolderLink.href = folderHref;
+            snippetFolderLink.textContent = 'Open snippet folder';
+            snippetFolderLink.removeAttribute('aria-disabled');
+        } else {
+            snippetFolderLink.href = '#';
+            snippetFolderLink.textContent = 'Snippet folder unavailable';
+            snippetFolderLink.setAttribute('aria-disabled', 'true');
+        }
+    }
+
+    if (readmeHint) {
+        readmeHint.textContent = readmeHintText;
+    }
+
+    if (readmeContent) {
+        readmeContent.innerHTML = readmeHtml;
+    }
+};
+
+const loadSnippet = async (problemId) => {
+    if (!problemId) {
+        setSnippetState({
+            hint: 'No problem id available for this row.',
+            code: '',
+            readmeHintText: 'README cannot be loaded without a problem id.',
+            readmeHtml: ''
+        });
+        return;
+    }
+
+    const fileName = `call_${problemId}.py`;
+    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/${encodeURIComponent(fileName)}`;
+    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/README.md`;
+    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(problemId)}`;
+
+    setSnippetState({
+        path: `${problemId}/${fileName}`,
+        hint: 'Loading snippet from repository...',
+        code: '',
+        folderHref: folderUrl,
+        readmeHintText: 'Loading README.md from repository...',
+        readmeHtml: ''
+    });
+
+    if (snippetCache.has(problemId)) {
+        const cached = snippetCache.get(problemId);
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: cached.codeHint,
+            code: cached.code,
+            folderHref: folderUrl,
+            readmeHintText: cached.readmeHint,
+            readmeHtml: cached.readmeHtml
+        });
+        return;
+    }
+
+    try {
+        const [snippetResponse, readmeResponse] = await Promise.all([
+            fetch(rawUrl),
+            fetch(readmeRawUrl)
+        ]);
+
+        let code = '';
+        let codeHint = '';
+        if (snippetResponse.status === 404) {
+            codeHint = 'No snippet file found for this problem id in the code_snippets branch.';
+        } else if (!snippetResponse.ok) {
+            codeHint = `Unable to fetch snippet (HTTP ${snippetResponse.status}).`;
+        } else {
+            code = await snippetResponse.text();
+            codeHint = 'Snippet found in code_snippets branch.';
+        }
+
+        let readme = '';
+        let readmeHtml = '';
+        let readmeHintText = '';
+        if (readmeResponse.status === 404) {
+            readmeHintText = 'No README.md found in this snippet folder.';
+        } else if (!readmeResponse.ok) {
+            readmeHintText = `Unable to fetch README.md (HTTP ${readmeResponse.status}).`;
+        } else {
+            readme = await readmeResponse.text();
+            readmeHtml = await renderMarkdownToHtml(readme);
+            readmeHintText = 'README.md loaded from snippet folder.';
+        }
+
+        snippetCache.set(problemId, {
+            code,
+            codeHint,
+            readmeHint: readmeHintText,
+            readmeHtml
+        });
+
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: codeHint,
+            code,
+            folderHref: folderUrl,
+            readmeHintText,
+            readmeHtml
+        });
+    } catch (error) {
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: 'Network error while fetching snippet.',
+            code: '',
+            folderHref: folderUrl,
+            readmeHintText: 'Network error while fetching README.md.',
+            readmeHtml: ''
+        });
+    }
+};
+
 const renderRowDetails = (rowApi) => {
     const rowData = rowApi.data();
     if (!rowData || !detailsContent) {
@@ -1377,6 +1587,10 @@ <h3 class="details-title">Problem details</h3>
 
         targetRow.classList.add('is-active');
         renderRowDetails(rowApi);
+
+        const name = stripHtml(rowApi.data()[0]);
+        const problemId = targetRow.getAttribute('data-problem-id') || toProblemId(name);
+        loadSnippet(problemId);
     });
 }
 
diff --git a/docs/javascript.html b/docs/javascript.html
index 73f9e58..80dde81 100644
--- a/docs/javascript.html
+++ b/docs/javascript.html
@@ -1,6 +1,11 @@
 
 
 <script>
+const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/code_snippets/code_snippets';
+const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/code_snippets/code_snippets';
+const snippetCache = new Map();
+let markdownParserPromise = null;
+
 const table = new DataTable('#problems', {
     layout: {
         topStart: 'search',
@@ -42,6 +47,12 @@
 
 const detailsHint = document.getElementById('problem-details-hint');
 const detailsContent = document.getElementById('problem-details-content');
+const snippetPath = document.getElementById('problem-snippet-path');
+const snippetHint = document.getElementById('problem-snippet-hint');
+const snippetContent = document.getElementById('problem-snippet-content');
+const snippetFolderLink = document.getElementById('problem-snippet-folder-link');
+const readmeHint = document.getElementById('problem-readme-hint');
+const readmeContent = document.getElementById('problem-readme-content');
 
 const stripHtml = (value) => {
     const wrapper = document.createElement('div');
@@ -55,6 +66,196 @@
     return wrapper.innerHTML;
 };
 
+const ensureMarkdownParser = () => {
+    if (window.marked?.parse) {
+        return Promise.resolve(window.marked.parse);
+    }
+
+    if (markdownParserPromise) {
+        return markdownParserPromise;
+    }
+
+    markdownParserPromise = new Promise((resolve, reject) => {
+        const script = document.createElement('script');
+        script.src = 'https://cdn.jsdelivr.net/npm/marked/marked.min.js';
+        script.async = true;
+        script.onload = () => {
+            if (window.marked?.parse) {
+                resolve(window.marked.parse);
+            } else {
+                reject(new Error('Marked loaded without parse function'));
+            }
+        };
+        script.onerror = () => reject(new Error('Failed to load marked library'));
+        document.head.appendChild(script);
+    });
+
+    return markdownParserPromise;
+};
+
+const renderMarkdownToHtml = async (markdownText) => {
+    if (!markdownText) {
+        return '';
+    }
+
+    try {
+        const parse = await ensureMarkdownParser();
+        return parse(markdownText);
+    } catch (error) {
+        return `<pre><code>${escapeHtml(markdownText)}</code></pre>`;
+    }
+};
+
+const toProblemId = (value) => {
+    if (!value) {
+        return '';
+    }
+
+    return value
+        .toLowerCase()
+        .trim()
+        .replace(/[-\s]+/g, '_')
+        .replace(/[^a-z0-9_]/g, '')
+        .replace(/_+/g, '_')
+        .replace(/^_+|_+$/g, '');
+};
+
+const setSnippetState = ({
+    path = '',
+    hint = '',
+    code = '',
+    folderHref = '',
+    readmeHintText = '',
+    readmeHtml = ''
+}) => {
+    if (snippetPath) {
+        snippetPath.textContent = path || 'call_{problem_id}.py';
+    }
+
+    if (snippetHint) {
+        snippetHint.textContent = hint;
+    }
+
+    if (snippetContent) {
+        snippetContent.textContent = code;
+    }
+
+    if (snippetFolderLink) {
+        if (folderHref) {
+            snippetFolderLink.href = folderHref;
+            snippetFolderLink.textContent = 'Open snippet folder';
+            snippetFolderLink.removeAttribute('aria-disabled');
+        } else {
+            snippetFolderLink.href = '#';
+            snippetFolderLink.textContent = 'Snippet folder unavailable';
+            snippetFolderLink.setAttribute('aria-disabled', 'true');
+        }
+    }
+
+    if (readmeHint) {
+        readmeHint.textContent = readmeHintText;
+    }
+
+    if (readmeContent) {
+        readmeContent.innerHTML = readmeHtml;
+    }
+};
+
+const loadSnippet = async (problemId) => {
+    if (!problemId) {
+        setSnippetState({
+            hint: 'No problem id available for this row.',
+            code: '',
+            readmeHintText: 'README cannot be loaded without a problem id.',
+            readmeHtml: ''
+        });
+        return;
+    }
+
+    const fileName = `call_${problemId}.py`;
+    const rawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/${encodeURIComponent(fileName)}`;
+    const readmeRawUrl = `${SNIPPET_BASE_URL}/${encodeURIComponent(problemId)}/README.md`;
+    const folderUrl = `${SNIPPET_TREE_BASE_URL}/${encodeURIComponent(problemId)}`;
+
+    setSnippetState({
+        path: `${problemId}/${fileName}`,
+        hint: 'Loading snippet from repository...',
+        code: '',
+        folderHref: folderUrl,
+        readmeHintText: 'Loading README.md from repository...',
+        readmeHtml: ''
+    });
+
+    if (snippetCache.has(problemId)) {
+        const cached = snippetCache.get(problemId);
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: cached.codeHint,
+            code: cached.code,
+            folderHref: folderUrl,
+            readmeHintText: cached.readmeHint,
+            readmeHtml: cached.readmeHtml
+        });
+        return;
+    }
+
+    try {
+        const [snippetResponse, readmeResponse] = await Promise.all([
+            fetch(rawUrl),
+            fetch(readmeRawUrl)
+        ]);
+
+        let code = '';
+        let codeHint = '';
+        if (snippetResponse.status === 404) {
+            codeHint = 'No snippet file found for this problem id in the code_snippets branch.';
+        } else if (!snippetResponse.ok) {
+            codeHint = `Unable to fetch snippet (HTTP ${snippetResponse.status}).`;
+        } else {
+            code = await snippetResponse.text();
+            codeHint = 'Snippet found in code_snippets branch.';
+        }
+
+        let readme = '';
+        let readmeHtml = '';
+        let readmeHintText = '';
+        if (readmeResponse.status === 404) {
+            readmeHintText = 'No README.md found in this snippet folder.';
+        } else if (!readmeResponse.ok) {
+            readmeHintText = `Unable to fetch README.md (HTTP ${readmeResponse.status}).`;
+        } else {
+            readme = await readmeResponse.text();
+            readmeHtml = await renderMarkdownToHtml(readme);
+            readmeHintText = 'README.md loaded from snippet folder.';
+        }
+
+        snippetCache.set(problemId, {
+            code,
+            codeHint,
+            readmeHint: readmeHintText,
+            readmeHtml
+        });
+
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: codeHint,
+            code,
+            folderHref: folderUrl,
+            readmeHintText,
+            readmeHtml
+        });
+    } catch (error) {
+        setSnippetState({
+            path: `${problemId}/${fileName}`,
+            hint: 'Network error while fetching snippet.',
+            code: '',
+            folderHref: folderUrl,
+            readmeHintText: 'Network error while fetching README.md.',
+            readmeHtml: ''
+        });
+    }
+};
+
 const renderRowDetails = (rowApi) => {
     const rowData = rowApi.data();
     if (!rowData || !detailsContent) {
@@ -140,6 +341,10 @@
 
         targetRow.classList.add('is-active');
         renderRowDetails(rowApi);
+
+        const name = stripHtml(rowApi.data()[0]);
+        const problemId = targetRow.getAttribute('data-problem-id') || toProblemId(name);
+        loadSnippet(problemId);
     });
 }
 
diff --git a/docs/problems.html b/docs/problems.html
index da33017..9869979 100644
--- a/docs/problems.html
+++ b/docs/problems.html
@@ -29,7 +29,7 @@
     </tr>
   </thead>
   <tbody>
-    <tr>
+    <tr data-problem-id="bbob">
       <td>BBOB</td>
       <td></td>
       <td>suite</td>
@@ -44,7 +44,7 @@
       <td><a href="https://doi.org/10.1080/10556788.2020.1808977">https://doi.org/10.1080/10556788.2020.1808977</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj">
       <td>BBOB-biobj</td>
       <td></td>
       <td>suite</td>
@@ -59,7 +59,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.1604.00359">https://doi.org/10.48550/arXiv.1604.00359</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_noisy">
       <td>BBOB-noisy</td>
       <td></td>
       <td>suite</td>
@@ -74,7 +74,7 @@
       <td><a href="https://hal.inria.fr/inria-00369466">https://hal.inria.fr/inria-00369466</a></td>
       <td><a href="https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads">https://web.archive.org/web/20210416065610/https://coco.gforge.inria.fr/doku.php?id=downloads</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_largescale">
       <td>BBOB-largescale</td>
       <td></td>
       <td>suite</td>
@@ -89,7 +89,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.1903.06396">https://doi.org/10.48550/arXiv.1903.06396</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_mixint">
       <td>BBOB-mixint</td>
       <td></td>
       <td>suite</td>
@@ -104,7 +104,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_biobj_mixint">
       <td>BBOB-biobj-mixint</td>
       <td></td>
       <td>suite</td>
@@ -119,7 +119,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321868">https://doi.org/10.1145/3321707.3321868</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="bbob_constrained">
       <td>BBOB-constrained</td>
       <td></td>
       <td>suite</td>
@@ -134,7 +134,7 @@
       <td><a href="http://numbbo.github.io/coco-doc/bbob-constrained/">http://numbbo.github.io/coco-doc/bbob-constrained/</a></td>
       <td><a href="https://github.com/numbbo/coco">https://github.com/numbbo/coco</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="morepo">
       <td>MOrepo</td>
       <td></td>
       <td>suite</td>
@@ -149,7 +149,7 @@
       <td></td>
       <td><a href="https://github.com/MCDMSociety/MOrepo">https://github.com/MCDMSociety/MOrepo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="zdt">
       <td>ZDT</td>
       <td></td>
       <td>suite</td>
@@ -164,7 +164,7 @@
       <td><a href="https://doi.org/10.1162/106365600568202">https://doi.org/10.1162/106365600568202</a></td>
       <td><a href="https://github.com/anyoptimization/pymoo">https://github.com/anyoptimization/pymoo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dtlz">
       <td>DTLZ</td>
       <td></td>
       <td>suite</td>
@@ -179,7 +179,7 @@
       <td><a href="https://doi.org/10.1109/CEC.2002.1007032">https://doi.org/10.1109/CEC.2002.1007032</a></td>
       <td><a href="https://pymoo.org/problems/many/dtlz.html">https://pymoo.org/problems/many/dtlz.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="wfg">
       <td>WFG</td>
       <td></td>
       <td>suite</td>
@@ -194,7 +194,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2005.861417">https://doi.org/10.1109/TEVC.2005.861417</a></td>
       <td><a href="https://pymoo.org/problems/many/wfg.html">https://pymoo.org/problems/many/wfg.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cdmp">
       <td>CDMP</td>
       <td></td>
       <td>suite</td>
@@ -209,7 +209,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321878">https://doi.org/10.1145/3321707.3321878</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sdp">
       <td>SDP</td>
       <td></td>
       <td>suite</td>
@@ -224,7 +224,7 @@
       <td><a href="https://doi.org/10.1109/TCYB.2019.2896021">https://doi.org/10.1109/TCYB.2019.2896021</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="maop">
       <td>MaOP</td>
       <td></td>
       <td>suite</td>
@@ -239,7 +239,7 @@
       <td><a href="https://doi.org/10.1016/j.swevo.2019.02.003">https://doi.org/10.1016/j.swevo.2019.02.003</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bp">
       <td>BP</td>
       <td></td>
       <td>suite</td>
@@ -254,7 +254,7 @@
       <td><a href="https://doi.org/10.1109/CEC.2019.8790277">https://doi.org/10.1109/CEC.2019.8790277</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="gpd">
       <td>GPD</td>
       <td></td>
       <td>generator</td>
@@ -269,7 +269,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106139">https://doi.org/10.1016/j.asoc.2020.106139</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="etmof">
       <td>ETMOF</td>
       <td></td>
       <td>suite</td>
@@ -284,7 +284,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.2110.08033">https://doi.org/10.48550/arXiv.2110.08033</a></td>
       <td><a href="https://github.com/songbai-liu/etmo">https://github.com/songbai-liu/etmo</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mmopp">
       <td>MMOPP</td>
       <td></td>
       <td>suite</td>
@@ -299,7 +299,7 @@
       <td><a href="http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412">http://www5.zzu.edu.cn/system/_content/download.jsp?urltype=news.DownloadAttachUrl&owner=1327567121&wbfileid=4764412</a></td>
       <td><a href="http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm">http://www5.zzu.edu.cn/ecilab/info/1036/1251.htm</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cfd">
       <td>CFD</td>
       <td>expensive evaluations 30s-15m</td>
       <td>suite</td>
@@ -314,7 +314,7 @@
       <td><a href="https://doi.org/10.1007/978-3-319-99259-4_24">https://doi.org/10.1007/978-3-319-99259-4_24</a></td>
       <td><a href="https://bitbucket.org/arahat/cfd-test-problem-suite">https://bitbucket.org/arahat/cfd-test-problem-suite</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gbea">
       <td>GBEA</td>
       <td>expensive evaluations 5s-35s, RW-GAN-Mario and TopTrumps are part of GBEA</td>
       <td>suite</td>
@@ -329,7 +329,7 @@
       <td><a href="https://doi.org/10.1145/3321707.3321805">https://doi.org/10.1145/3321707.3321805</a></td>
       <td><a href="https://github.com/ttusar/coco-gbea">https://github.com/ttusar/coco-gbea</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="car_structure">
       <td>Car structure</td>
       <td>54 constraints</td>
       <td>suite</td>
@@ -344,7 +344,7 @@
       <td><a href="https://doi.org/10.1145/3205651.3205702">https://doi.org/10.1145/3205651.3205702</a></td>
       <td><a href="http://ladse.eng.isas.jaxa.jp/benchmark/">http://ladse.eng.isas.jaxa.jp/benchmark/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="emo2017">
       <td>EMO2017</td>
       <td></td>
       <td>suite</td>
@@ -359,7 +359,7 @@
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/</a></td>
       <td><a href="https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip">https://www.ini.rub.de/PEOPLE/glasmtbl/projects/bbcomp/downloads/realworld-problems-bbcomp-EMO-2017.zip</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="jsec2019">
       <td>JSEC2019</td>
       <td>expensive evaluations 3s; 22 constraints</td>
       <td>single</td>
@@ -374,7 +374,7 @@
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
       <td><a href="http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html">http://www.jpnsec.org/files/competition2019/EC-Symposium-2019-Competition-English.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="re">
       <td>RE</td>
       <td></td>
       <td>suite</td>
@@ -389,7 +389,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cre">
       <td>CRE</td>
       <td></td>
       <td>suite</td>
@@ -404,7 +404,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2020.106078">https://doi.org/10.1016/j.asoc.2020.106078</a></td>
       <td><a href="https://github.com/ryojitanabe/reproblems">https://github.com/ryojitanabe/reproblems</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="radar_waveform">
       <td>Radar waveform</td>
       <td></td>
       <td>single</td>
@@ -419,7 +419,7 @@
       <td><a href="https://doi.org/10.1007/978-3-540-70928-2_53">https://doi.org/10.1007/978-3-540-70928-2_53</a></td>
       <td><a href="http://code.evanhughes.org/">http://code.evanhughes.org/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mf2">
       <td>MF2</td>
       <td></td>
       <td>suite</td>
@@ -434,7 +434,7 @@
       <td><a href="https://doi.org/10.21105/joss.02049">https://doi.org/10.21105/joss.02049</a></td>
       <td><a href="https://github.com/sjvrijn/mf2">https://github.com/sjvrijn/mf2</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="amvop">
       <td>AMVOP</td>
       <td></td>
       <td>suite</td>
@@ -449,7 +449,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="rwmvop">
       <td>RWMVOP</td>
       <td></td>
       <td>suite</td>
@@ -464,7 +464,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281531">https://doi.org/10.1109/TEVC.2013.2281531</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="sbox_cost">
       <td>SBOX-COST</td>
       <td>problems from BBOB but allows instances with the optimum close to the boundary</td>
       <td>suite</td>
@@ -479,7 +479,7 @@
       <td><a href="https://doi.org/10.48550/arXiv.2305.12221">https://doi.org/10.48550/arXiv.2305.12221</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/">https://github.com/IOHprofiler/IOHexperimenter/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mnk_landscapes">
       <td>ρMNK-Landscapes</td>
       <td>tunable variable and objective dimensions; tunable multimodality and correlation between objectives</td>
       <td>generator</td>
@@ -494,7 +494,7 @@
       <td><a href="https://doi.org/10.1016/j.ejor.2012.12.019">https://doi.org/10.1016/j.ejor.2012.12.019</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mubqp">
       <td>mUBQP</td>
       <td>tunable variable and objective dimensions; tunable density and correlation between objectives</td>
       <td>generator</td>
@@ -509,7 +509,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2013.11.008">https://doi.org/10.1016/j.asoc.2013.11.008</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mtsp">
       <td>ρmTSP</td>
       <td>tunable variable and objective dimensions; tunable instance type (euclidian/random); tunable correlation between objectives</td>
       <td>generator</td>
@@ -524,7 +524,7 @@
       <td><a href="https://doi.org/10.1007/978-3-319-45823-6_40">https://doi.org/10.1007/978-3-319-45823-6_40</a></td>
       <td><a href="https://gitlab.com/aliefooghe/mocobench/">https://gitlab.com/aliefooghe/mocobench/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2015_dmoo">
       <td>CEC2015-DMOO</td>
       <td></td>
       <td>suite</td>
@@ -539,7 +539,7 @@
       <td>Benchmark Functions for CEC 2015 Special Session and Competition on Dynamic Multi-objective Optimization</td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ealain">
       <td>Ealain</td>
       <td>Real-world-like, easily extensible to increase complexity</td>
       <td>generator</td>
@@ -554,7 +554,7 @@
       <td><a href="https://doi.org/10.1145/3638530.3654299">https://doi.org/10.1145/3638530.3654299</a></td>
       <td><a href="https://github.com/qrenau/Ealain">https://github.com/qrenau/Ealain</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ma_bbob">
       <td>MA-BBOB</td>
       <td>Generator that creates affine combinations of BBOB functions</td>
       <td>generator</td>
@@ -569,7 +569,7 @@
       <td><a href="https://doi.org/10.1145/3673908">https://doi.org/10.1145/3673908</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb">https://github.com/IOHprofiler/IOHexperimenter/blob/master/example/Competitions/MA-BBOB/Example_MABBOB.ipynb</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mpm2">
       <td>MPM2</td>
       <td>nonlinear nonseparable nonsymmetric; scalable in terms of time to evaluate the objective function</td>
       <td>generator</td>
@@ -584,7 +584,7 @@
       <td><a href="https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf">https://ls11-www.cs.tu-dortmund.de/_media/techreports/tr15-01.pdf</a></td>
       <td><a href="https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py">https://github.com/jakobbossek/smoof/blob/master/inst/mpm2.py</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="convex_dtlz2">
       <td>Convex DTLZ2</td>
       <td>Variant of DTLZ2 with a convex Pareto front (instead of concave)</td>
       <td>single</td>
@@ -599,7 +599,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281535">https://doi.org/10.1109/TEVC.2013.2281535</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inverted_dtlz1">
       <td>Inverted DTLZ1</td>
       <td>Variant of DTLZ1 with an inverted Pareto front</td>
       <td>single</td>
@@ -614,7 +614,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2013.2281534">https://doi.org/10.1109/TEVC.2013.2281534</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_dtlz">
       <td>Minus DTLZ</td>
       <td>Variant of DTLZ that minimises the inverse of the base DTLZ functions</td>
       <td>suite</td>
@@ -629,7 +629,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="minus_wfg">
       <td>Minus WFG</td>
       <td>Variant of WFG that minimises the inverse of the base WFG functions</td>
       <td>suite</td>
@@ -644,7 +644,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2016.2587749">https://doi.org/10.1109/TEVC.2016.2587749</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l1_zdt">
       <td>L1-ZDT</td>
       <td>Variant of ZDT with linkages between variables within one of two groups but not between variables in a different group; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -659,7 +659,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_zdt">
       <td>L2-ZDT</td>
       <td>Variant of ZDT with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -674,7 +674,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_zdt">
       <td>L3-ZDT</td>
       <td>Variant of L2-ZDT using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -689,7 +689,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l2_dtlz">
       <td>L2-DTLZ</td>
       <td>Variant of DTLZ2 and DTLZ3 with linkages between all variables; Linear recombination operators can potentially take advantage of the problem structure</td>
       <td>suite</td>
@@ -704,7 +704,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="l3_dtlz">
       <td>L3-DTLZ</td>
       <td>Variant of L2-DTLZ using a mapping to prevent linear recombination operators from potentially taking advantage of the problem structure</td>
       <td>suite</td>
@@ -719,7 +719,7 @@
       <td><a href="https://doi.org/10.1145/1143997.1144179">https://doi.org/10.1145/1143997.1144179</a></td>
       <td>?</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2018_dt_cec2018_competition_on_dynamic_multiobjective_optimisation">
       <td>CEC2018 DT - CEC2018 Competition on Dynamic Multiobjective Optimisation</td>
       <td>14 problems. Time-dependent: Pareto front/Pareto set geometry; irregular Pareto front shapes; variable-linkage; number of disconnected Pareto front segments; etc.</td>
       <td>suite</td>
@@ -734,7 +734,7 @@
       <td><a href="https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf">https://www.academia.edu/download/94499025/TR-CEC2018-DMOP-Competition.pdf</a></td>
       <td><a href="https://pymoo.org/problems/dynamic/df.html">https://pymoo.org/problems/dynamic/df.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="modact_multiobjective_design_of_actuators">
       <td>MODAct - multiobjective design of actuators</td>
       <td>Realistic Constrained Multi-Objective Optimization Benchmark Problems from Design. Need the <a href="https://github.com/epfl-lamd/modact">https://github.com/epfl-lamd/modact</a> package installed; evaluation times around 20ms</td>
       <td>suite</td>
@@ -749,7 +749,7 @@
       <td><a href="https://doi.org/10.1109/TEVC.2020.3020046">https://doi.org/10.1109/TEVC.2020.3020046</a></td>
       <td><a href="https://pymoo.org/problems/constrained/modact.html">https://pymoo.org/problems/constrained/modact.html</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="iohclustering">
       <td>IOHClustering</td>
       <td>Set of benchmark problems from clustering: optimization task is selecting cluster centers for a given set of data, with the number of clusters defining problem dimensionality. Includes both a suite and a generator. Based on ML clustering datasets</td>
       <td>suite; generator</td>
@@ -764,7 +764,7 @@
       <td><a href="https://arxiv.org/pdf/2505.09233">https://arxiv.org/pdf/2505.09233</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHClustering">https://github.com/IOHprofiler/IOHClustering</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg_ii">
       <td>GNBG-II</td>
       <td>Generalized Numerical Benchmark Generator (version 2). Also in IOH <a href="https://github.com/IOHprofiler/IOHGNBG">https://github.com/IOHprofiler/IOHGNBG</a></td>
       <td>suite; generator</td>
@@ -779,7 +779,7 @@
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3712255.3734271">https://dl.acm.org/doi/pdf/10.1145/3712255.3734271</a></td>
       <td><a href="https://github.com/rohitsalgotra/GNBG-II">https://github.com/rohitsalgotra/GNBG-II</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gnbg">
       <td>GNBG</td>
       <td>Generalized Numerical Benchmark Generator</td>
       <td>suite; generator</td>
@@ -794,7 +794,7 @@
       <td><a href="https://arxiv.org/abs/2312.07083">https://arxiv.org/abs/2312.07083</a></td>
       <td><a href="https://github.com/Danial-Yazdani/GNBG-Generator">https://github.com/Danial-Yazdani/GNBG-Generator</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="dynamicbinval">
       <td>DynamicBinVal</td>
       <td>Four versions of the dynamic binary value problem</td>
       <td>suite</td>
@@ -809,7 +809,7 @@
       <td><a href="https://arxiv.org/pdf/2404.15837">https://arxiv.org/pdf/2404.15837</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="pbo">
       <td>PBO</td>
       <td>Suite of 25 binary optimization problems</td>
       <td>suite</td>
@@ -824,7 +824,7 @@
       <td><a href="https://dl.acm.org/doi/pdf/10.1145/3319619.3326810">https://dl.acm.org/doi/pdf/10.1145/3319619.3326810</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="w_model">
       <td>W-model</td>
       <td>Tunable generator for binary optimization based on several difficulty features</td>
       <td>generator</td>
@@ -839,7 +839,7 @@
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw">https://dl.acm.org/doi/abs/10.1145/3205651.3208240?casa_token=S4U_Pi9f6MwAAAAA:U9ztNTPwmupT8K3GamWZfBL7-8fqjxPtr_kprv51vdwA-REsp0EyOFGa99BtbANb0XbqyrVg795hIw</a></td>
       <td><a href="https://github.com/thomasWeise/BBDOB_W_Model">https://github.com/thomasWeise/BBDOB_W_Model</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="submodular_optimitzation">
       <td>Submodular Optimitzation</td>
       <td>set of graph-based submodular optimization problems from 4 problem types</td>
       <td>suite</td>
@@ -854,7 +854,7 @@
       <td><a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181">https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=10254181</a></td>
       <td><a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2013">
       <td>CEC2013</td>
       <td>suite used for cec2013 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -869,7 +869,7 @@
       <td><a href="https://peerj.com/articles/cs-2671/CEC2013.pdf">https://peerj.com/articles/cs-2671/CEC2013.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/CEC2013">https://github.com/P-N-Suganthan/CEC2013</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cec2022">
       <td>CEC2022</td>
       <td>suite used for cec2022 competition. Also in IOH <a href="https://github.com/IOHprofiler/IOHexperimenter">https://github.com/IOHprofiler/IOHexperimenter</a></td>
       <td>suite</td>
@@ -884,7 +884,7 @@
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf">https://github.com/P-N-Suganthan/2022-SO-BO/blob/main/CEC2022%20TR.pdf</a></td>
       <td><a href="https://github.com/P-N-Suganthan/2022-SO-BO">https://github.com/P-N-Suganthan/2022-SO-BO</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_zeromaxsphere">
       <td>Onemax+Sphere / Zeromax+Sphere</td>
       <td></td>
       <td>single</td>
@@ -899,7 +899,7 @@
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="onemaxsphere_deceptivetraprotatedellipsoid">
       <td>Onemax+Sphere / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -914,7 +914,7 @@
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="inversedeceptivetraprotatedellipsoid_deceptivetraprotatedellipsoid">
       <td>InverseDeceptiveTrap+RotatedEllipsoid / DeceptiveTrap+RotatedEllipsoid</td>
       <td></td>
       <td>single</td>
@@ -929,7 +929,7 @@
       <td><a href="https://doi.org/10.1145/3449726.3459521">https://doi.org/10.1145/3449726.3459521</a></td>
       <td>None</td>
     </tr>
-    <tr>
+    <tr data-problem-id="porkchopplotinterplanetarytrajectory">
       <td>PorkchopPlotInterplanetaryTrajectory</td>
       <td></td>
       <td>suite</td>
@@ -944,7 +944,7 @@
       <td><a href="https://doi.org/10.1109/CEC65147.2025.11042973">https://doi.org/10.1109/CEC65147.2025.11042973</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="kinematicsrobotarm">
       <td>KinematicsRobotArm</td>
       <td></td>
       <td>suite</td>
@@ -959,7 +959,7 @@
       <td><a href="https://doi.org/10.1023/A:1013258808932">https://doi.org/10.1023/A:1013258808932</a></td>
       <td><a href="https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world">https://github.com/ShuaiqunPan/Transfer_Random_forests_BBOB_Real_world</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="vehicledynamics">
       <td>VehicleDynamics</td>
       <td></td>
       <td>suite</td>
@@ -974,7 +974,7 @@
       <td><a href="https://www.scitepress.org/Papers/2023/121580/121580.pdf">https://www.scitepress.org/Papers/2023/121580/121580.pdf</a></td>
       <td><a href="https://zenodo.org/records/8307853">https://zenodo.org/records/8307853</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="mechbench">
       <td>MECHBench</td>
       <td>This is a set of problems with inspiration from Structural Mechanics Design Optimization. The suite comprises three physical models, from which the user may define different kind of problems which impact the final design output.</td>
       <td>Problem Suite</td>
@@ -989,7 +989,7 @@
       <td><a href="https://arxiv.org/abs/2511.10821">https://arxiv.org/abs/2511.10821</a></td>
       <td><a href="https://github.com/BayesOptApp/MECHBench">https://github.com/BayesOptApp/MECHBench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="expobench">
       <td>EXPObench</td>
       <td>Wind farm layout optimization, gas filter design, pipe shape optimization, hyperparameter tuning, and hospital simulation</td>
       <td>Problem Suite</td>
@@ -1004,7 +1004,7 @@
       <td><a href="https://doi.org/10.1016/j.asoc.2023.110744">https://doi.org/10.1016/j.asoc.2023.110744</a></td>
       <td><a href="https://github.com/AlgTUDelft/ExpensiveOptimBenchmark">https://github.com/AlgTUDelft/ExpensiveOptimBenchmark</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="gasoline_direct_injection_engine_design">
       <td>Gasoline direct injection engine design</td>
       <td>A multi-objective optimization problem seeking to minimize fuel consumption and NOx emissions over a two-minute dynamic duty cycle, subject to five constraints (turbine inlet temperature, number of knock occurrences, peak cylinder pressure, peak cylinder pressure rise, total work). Seven decision variables are defined: four define the hardware choices of cylinder compression ratio, turbo machinery and EGR cooler sizing; three relate to control variables that parameterise the engine control logic.</td>
       <td>Single Problem</td>
@@ -1019,7 +1019,7 @@
       <td></td>
       <td><a href="https://doi.org/10.1016/j.ejor.2022.08.032">https://doi.org/10.1016/j.ejor.2022.08.032</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="beacon">
       <td>BEACON</td>
       <td>Generator for bi-objective benchmark problems with explicitly controlled correlations in continuous spaces.</td>
       <td>Generator</td>
@@ -1034,7 +1034,7 @@
       <td><a href="https://dl.acm.org/doi/10.1145/3712255.3734303">https://dl.acm.org/doi/10.1145/3712255.3734303</a></td>
       <td><a href="https://github.com/Stebbet/BEACON/">https://github.com/Stebbet/BEACON/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="tulipaenergy">
       <td>TulipaEnergy</td>
       <td>Determine the optimal investment and operation decisions  for different types of assets in the energy system (production, consumption, conversion, storage, and transport), while minimizing loss of load.</td>
       <td>Problem Suite</td>
@@ -1049,7 +1049,7 @@
       <td>See <a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/40-scientific-foundation/45-scientific-references</a></td>
       <td><a href="https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/">https://tulipaenergy.github.io/TulipaEnergyModel.jl/stable/</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="ato">
       <td>ATO</td>
       <td>Parameters of the Modules of the Automatic Train Operation should be optimized. The parameters are continuous with different ranges. There are two objectives (minimizing energy consumption, minimizing driving duration.</td>
       <td>Single Problem</td>
@@ -1064,7 +1064,7 @@
       <td></td>
       <td>-</td>
     </tr>
-    <tr>
+    <tr data-problem-id="brachytherapy_treatment_planning">
       <td>Brachytherapy treatment planning</td>
       <td>Treatment planning for internal radiation therapy</td>
       <td>Problem Suite</td>
@@ -1079,7 +1079,7 @@
       <td><a href="https://www.sciencedirect.com/science/article/pii/S1538472123016781">https://www.sciencedirect.com/science/article/pii/S1538472123016781</a></td>
       <td></td>
     </tr>
-    <tr>
+    <tr data-problem-id="fleetopt">
       <td>FleetOpt</td>
       <td>Healthcare organisation in the UK provided data about their current fleet of vehicles to conduct non-emergency heathcare trips in the Argyll and Bute region of Scotland, UK. They also provided historical data about the trips the vehicles took and about the bases which the vehicles return to. The aim is to reduce the existing fleet of vehicles while still ensuring all trips can be covered. Moving a vehicle from one base to another to help cover trips is OK as long as the original base can still cover its trips. Link to paper with more details: <a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Single Problem</td>
@@ -1094,7 +1094,7 @@
       <td><a href="https://dl.acm.org/doi/abs/10.1145/3638530.3664137">https://dl.acm.org/doi/abs/10.1145/3638530.3664137</a></td>
       <td>Not public: was done for real client with their private data</td>
     </tr>
-    <tr>
+    <tr data-problem-id="building_spatial_design">
       <td>Building spatial design</td>
       <td>Optimise the spatial layout of a building to: minimise energy consumption for climate control, and minimise the strain on the structure</td>
       <td>Single Problem</td>
@@ -1109,7 +1109,7 @@
       <td><a href="https://hdl.handle.net/1887/81789">https://hdl.handle.net/1887/81789</a></td>
       <td><a href="https://github.com/TUe-excellent-buildings/BSO-toolbox">https://github.com/TUe-excellent-buildings/BSO-toolbox</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="electric_motor_design_optimization">
       <td>Electric Motor Design Optimization</td>
       <td>The goal is to find a design of a synchronous electric motor for power steering systems that minimizes costs and satisfies all constraints.</td>
       <td>Single Problem</td>
@@ -1124,7 +1124,7 @@
       <td><a href="https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf">https://dis.ijs.si/tea/Publications/Tusar23Multistep.pdf</a> (paper in Slovene)</td>
       <td>Implementation not freely available</td>
     </tr>
-    <tr>
+    <tr data-problem-id="bono_bench">
       <td>BONO-Bench</td>
       <td>Bi-objective problem generator and suite with scalable continuous decision space. Features complex problem properties (different types of multimodality and challenges in decision and objective space) as well as Pareto front approximations with error guarantees for the hypervolume and exact R2 indicators.</td>
       <td>Generator</td>
@@ -1139,7 +1139,7 @@
       <td></td>
       <td><a href="https://github.com/schaepermeier/bonobench">https://github.com/schaepermeier/bonobench</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="randoptgen">
       <td>RandOptGen</td>
       <td>RandOptGen: A Unified Random Problem Generator for Single-and Multi-Objective Optimization Problems with Mixed-Variable Input Spaces</td>
       <td>Generator</td>
@@ -1154,7 +1154,7 @@
       <td></td>
       <td><a href="https://github.com/MALEO-research-group/RandOptGen">https://github.com/MALEO-research-group/RandOptGen</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="cuter">
       <td>CUTEr</td>
       <td>A constrained and unconstrained testing environment</td>
       <td>Problem Suite</td>
@@ -1169,7 +1169,7 @@
       <td><a href="https://dl.acm.org/doi/10.1145/962437.962439">https://dl.acm.org/doi/10.1145/962437.962439</a></td>
       <td>Not Found</td>
     </tr>
-    <tr>
+    <tr data-problem-id="cutest">
       <td>CUTEst</td>
       <td>The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software</td>
       <td>Problem Suite</td>
@@ -1184,7 +1184,7 @@
       <td><a href="https://link.springer.com/article/10.1007/s10589-014-9687-3">https://link.springer.com/article/10.1007/s10589-014-9687-3</a></td>
       <td><a href="https://github.com/jfowkes/pycutest">https://github.com/jfowkes/pycutest</a></td>
     </tr>
-    <tr>
+    <tr data-problem-id="puboi">
       <td>PUBOi</td>
       <td>A benchmark in which variable importance is tunable, based on the Walsh function</td>
       <td>Generator</td>
@@ -1208,4 +1208,13 @@
     <h3 class="details-title">Problem details</h3>
     <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
     <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
 </section>
diff --git a/docs/table_styles.css b/docs/table_styles.css
index 1c53f6d..6d3a9fd 100644
--- a/docs/table_styles.css
+++ b/docs/table_styles.css
@@ -257,6 +257,103 @@ h2 {
     font-style: italic;
 }
 
+.snippet-shell {
+    margin-top: 0.85rem;
+    border: 1px solid var(--opl-border);
+    border-radius: 10px;
+    background: #f8fbfe;
+    padding: 0.7rem;
+}
+
+.snippet-title {
+    margin: 0;
+    font-size: 0.84rem;
+    color: #23415e;
+}
+
+.snippet-hint {
+    margin: 0.4rem 0 0.55rem;
+    font-size: 0.82rem;
+    color: var(--opl-muted);
+}
+
+.snippet-meta {
+    margin: 0.35rem 0 0.45rem;
+    font-size: 0.82rem;
+}
+
+.snippet-meta a {
+    color: #0a5d74;
+    text-decoration: underline;
+}
+
+.snippet-meta a[aria-disabled="true"] {
+    color: var(--opl-muted);
+    pointer-events: none;
+    text-decoration: none;
+}
+
+.snippet-code {
+    margin: 0;
+    background: #11263b;
+    color: #e9f3ff;
+    border-radius: 8px;
+    padding: 0.7rem;
+    max-height: 320px;
+    overflow: auto;
+}
+
+.snippet-code code {
+    font-family: Consolas, "Courier New", monospace;
+    font-size: 0.8rem;
+    line-height: 1.45;
+    white-space: pre;
+}
+
+.snippet-readme {
+    margin-top: 0.35rem;
+    background: #ffffff;
+    border: 1px solid var(--opl-border);
+    border-radius: 8px;
+    padding: 0.75rem;
+    max-height: 380px;
+    overflow: auto;
+    font-size: 0.83rem;
+    line-height: 1.45;
+}
+
+.snippet-readme h1,
+.snippet-readme h2,
+.snippet-readme h3,
+.snippet-readme h4 {
+    margin: 0.7rem 0 0.45rem;
+    color: #18334e;
+}
+
+.snippet-readme p,
+.snippet-readme ul,
+.snippet-readme ol {
+    margin: 0.45rem 0;
+}
+
+.snippet-readme pre {
+    margin: 0.5rem 0;
+    background: #11263b;
+    color: #e9f3ff;
+    border-radius: 8px;
+    padding: 0.65rem;
+    overflow: auto;
+}
+
+.snippet-readme code {
+    font-family: Consolas, "Courier New", monospace;
+    font-size: 0.8rem;
+}
+
+.snippet-readme a {
+    color: #0a5d74;
+}
+
 @media (max-width: 900px) {
     body {
         margin: 0.6rem;
diff --git a/docs/table_template.html b/docs/table_template.html
index 270d61f..db77aac 100644
--- a/docs/table_template.html
+++ b/docs/table_template.html
@@ -18,4 +18,13 @@
     <h3 class="details-title">Problem details</h3>
     <p class="details-hint" id="problem-details-hint">Click a table row to inspect full details.</p>
     <dl class="details-grid" id="problem-details-content"></dl>
+    <div class="snippet-shell" id="problem-snippet-shell">
+        <h4 class="snippet-title">README.md</h4>
+        <p class="snippet-hint" id="problem-readme-hint">README content for this snippet folder will appear here.</p>
+        <div class="snippet-readme markdown-body" id="problem-readme-content"></div>
+        <h4 class="snippet-title">Snippet: <code id="problem-snippet-path">call_{problem_id}.py</code></h4>
+        <p class="snippet-meta"><a id="problem-snippet-folder-link" href="#" target="_blank" rel="noopener noreferrer">Open snippet folder</a></p>
+        <p class="snippet-hint" id="problem-snippet-hint">Select a problem to check for an available code snippet.</p>
+        <pre class="snippet-code"><code id="problem-snippet-content"></code></pre>
+    </div>
 </section>
diff --git a/yaml_to_html.py b/yaml_to_html.py
index 87e8cce..f106263 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -21,6 +21,32 @@ def repl(m):
 
     return URL_RE.sub(repl, value)
 
+
+def to_problem_id(value):
+    if not isinstance(value, str):
+        return ""
+
+    normalized = value.strip().lower().replace("-", "_").replace(" ", "_")
+    normalized = re.sub(r"[^a-z0-9_]", "", normalized)
+    normalized = re.sub(r"_+", "_", normalized).strip("_")
+    return normalized
+
+
+def add_problem_id_row_attributes(table_html, problem_ids):
+    marker = "<tbody>"
+    if marker not in table_html:
+        return table_html
+
+    body_start = table_html.index(marker) + len(marker)
+    prefix = table_html[:body_start]
+    body = table_html[body_start:]
+
+    for problem_id in problem_ids:
+        safe_id = escape(problem_id, quote=True)
+        body = body.replace("<tr>", f'<tr data-problem-id="{safe_id}">', 1)
+
+    return prefix + body
+
 yaml_file = "problems.yaml"
 
 html_dir = "docs/"
@@ -33,7 +59,15 @@ def repl(m):
 
 # Load data
 with open(yaml_file) as yaml_input:
-    data = pd.json_normalize(yaml.safe_load(yaml_input))
+    raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
+
+if "problem_id" in raw_data.columns:
+    problem_ids = raw_data["problem_id"].fillna("").map(str)
+else:
+    problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
+
+problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
+data = raw_data.copy()
 
 # Choose desired columns
 all_columns = False
@@ -66,6 +100,8 @@ def repl(m):
                      border=0,
                      na_rep="")  # Leave NaN cells empty
 
+table = add_problem_id_row_attributes(table, problem_ids.tolist())
+
 # Add footer to facilitate individual column search
 idx = table.index('</table>')
 final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]

From 32c8b8681f6511a93a654598cc0f568d2b6f7d7b Mon Sep 17 00:00:00 2001
From: Dvermetten <d.vermetten@gmail.com>
Date: Wed, 22 Apr 2026 14:17:31 +0200
Subject: [PATCH 2/2] Change links to main branch and add if__main__ things

---
 docs/javascript.html |   4 +-
 yaml_to_html.py      | 166 ++++++++++++++++++++++---------------------
 2 files changed, 86 insertions(+), 84 deletions(-)

diff --git a/docs/javascript.html b/docs/javascript.html
index 80dde81..d5f41ef 100644
--- a/docs/javascript.html
+++ b/docs/javascript.html
@@ -1,8 +1,8 @@
 
 
 <script>
-const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/code_snippets/code_snippets';
-const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/code_snippets/code_snippets';
+const SNIPPET_BASE_URL = 'https://raw.githubusercontent.com/OpenOptimizationOrg/OPL/main/code_snippets';
+const SNIPPET_TREE_BASE_URL = 'https://github.com/OpenOptimizationOrg/OPL/tree/main/code_snippets';
 const snippetCache = new Map();
 let markdownParserPromise = null;
 
diff --git a/yaml_to_html.py b/yaml_to_html.py
index f106263..639f00b 100755
--- a/yaml_to_html.py
+++ b/yaml_to_html.py
@@ -56,86 +56,88 @@ def add_problem_id_row_attributes(table_html, problem_ids):
 html_footer = f"{html_dir}footer.html"
 html_index = f"{html_dir}index.html"
 html_table_template = f"{html_dir}table_template.html"
-
-# Load data
-with open(yaml_file) as yaml_input:
-    raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
-
-if "problem_id" in raw_data.columns:
-    problem_ids = raw_data["problem_id"].fillna("").map(str)
-else:
-    problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
-
-problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
-data = raw_data.copy()
-
-# Choose desired columns
-all_columns = False
 default_columns = ["name",
-                   "textual description",
-                   "suite/generator/single",
-                   "objectives",
-                   "dimensionality",
-                   "variable type",
-                   "constraints",
-                   "dynamic",
-                   "noise",
-                   "multi-fidelity",
-                   "source (real-world/artificial)",
-                   "reference",
-                   "implementation"]
-
-if all_columns is False:
-    columns = default_columns
-    data = data[columns]
-
-data = data.map(linkify_cell)
-
-# Generate plain table
-table = data.to_html(render_links=False,
-                     escape=False,  # Don't escape HTML in cells (to allow links)
-                     index=False,
-                     table_id="problems",
-                     classes=["display compact", "display", "styled-table"],  # Set display style
-                     border=0,
-                     na_rep="")  # Leave NaN cells empty
-
-table = add_problem_id_row_attributes(table, problem_ids.tolist())
-
-# Add footer to facilitate individual column search
-idx = table.index('</table>')
-final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]
-
-default_hidden_columns = {"textual description", "reference", "implementation"}
-
-column_toggles = "".join(
-    [
-        (
-            f'<label class="column-chip">'
-            f'<input class="col-toggle" type="checkbox" data-column="{i}"'
-            f'{" checked" if col not in default_hidden_columns else ""}>'
-            f'<span>{escape(col)}</span>'
-            f'</label>'
-        )
-        for i, col in enumerate(data.columns)
-    ]
-)
-
-with open(html_table_template, encoding="utf-8") as template_file:
-    table_template = template_file.read()
-
-table_markup = (
-    table_template
-    .replace("__COLUMN_TOGGLES__", column_toggles)
-    .replace("__TABLE__", final_table)
-)
-
-# Write table to file
-with open(html_table, "w", encoding="utf-8") as table_file:
-    table_file.write(table_markup)
-
-# Merge table and scripts into HTML page
-with open(html_index, "wb") as output_file:
-    for part_path in [html_header, html_table, html_scripts, html_footer]:
-        with open(part_path, "rb") as part_file:
-            shutil.copyfileobj(part_file, output_file)
+                "textual description",
+                "suite/generator/single",
+                "objectives",
+                "dimensionality",
+                "variable type",
+                "constraints",
+                "dynamic",
+                "noise",
+                "multi-fidelity",
+                "source (real-world/artificial)",
+                "reference",
+                "implementation"]
+
+if __name__ == "__main__":
+
+    # Load data
+    with open(yaml_file) as yaml_input:
+        raw_data = pd.json_normalize(yaml.safe_load(yaml_input))
+
+    if "problem_id" in raw_data.columns:
+        problem_ids = raw_data["problem_id"].fillna("").map(str)
+    else:
+        problem_ids = raw_data["name"].fillna("").map(str).map(to_problem_id)
+
+    problem_ids = problem_ids.where(problem_ids.str.len() > 0, raw_data["name"].fillna("").map(str).map(to_problem_id))
+    data = raw_data.copy()
+
+    # Choose desired columns
+    all_columns = False
+
+    if all_columns is False:
+        columns = default_columns
+        data = data[columns]
+
+    data = data.map(linkify_cell)
+
+    # Generate plain table
+    table = data.to_html(render_links=False,
+                        escape=False,  # Don't escape HTML in cells (to allow links)
+                        index=False,
+                        table_id="problems",
+                        classes=["display compact", "display", "styled-table"],  # Set display style
+                        border=0,
+                        na_rep="")  # Leave NaN cells empty
+
+    table = add_problem_id_row_attributes(table, problem_ids.tolist())
+
+    # Add footer to facilitate individual column search
+    idx = table.index('</table>')
+    final_table = table[:idx] + "<tfoot><tr>" + " ".join(["<th>"+ i +"</th>" for i in data.columns])+"</tr> </tfoot>" + table[idx:]
+
+    default_hidden_columns = {"textual description", "reference", "implementation"}
+
+    column_toggles = "".join(
+        [
+            (
+                f'<label class="column-chip">'
+                f'<input class="col-toggle" type="checkbox" data-column="{i}"'
+                f'{" checked" if col not in default_hidden_columns else ""}>'
+                f'<span>{escape(col)}</span>'
+                f'</label>'
+            )
+            for i, col in enumerate(data.columns)
+        ]
+    )
+
+    with open(html_table_template, encoding="utf-8") as template_file:
+        table_template = template_file.read()
+
+    table_markup = (
+        table_template
+        .replace("__COLUMN_TOGGLES__", column_toggles)
+        .replace("__TABLE__", final_table)
+    )
+
+    # Write table to file
+    with open(html_table, "w", encoding="utf-8") as table_file:
+        table_file.write(table_markup)
+
+    # Merge table and scripts into HTML page
+    with open(html_index, "wb") as output_file:
+        for part_path in [html_header, html_table, html_scripts, html_footer]:
+            with open(part_path, "rb") as part_file:
+                shutil.copyfileobj(part_file, output_file)