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-<!DOCTYPE html><html><head><meta charset=utf-8><meta name=viewport content="width=device-width, initial-scale=1"><title>[NeurIPS 2024] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution</title><link rel=icon type=image/svg href=static/icon.svg><link rel=preconnect href=https://cdn.jsdelivr.net crossorigin><link rel=preconnect href=https://fonts.googleapis.com crossorigin><link rel=preconnect href=https://cdnjs.cloudflare.com crossorigin><link rel=preload href=static/index.css as=style><link rel=preload href=static/images/reevo.png as=image><link rel=preload href=static/images/show_reflec.png as=image><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/css/bulma.min.css integrity="sha256-WLKGWSIJYerRN8tbNGtXWVYnUM5wMJTXD8eG4NtGcDM=" crossorigin=anonymous><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bulma-carousel.min.js integrity="sha256-qKR77yzVBkDYKuoSg2BpAIbMtmF8aFjuTHFVnIjkpzI=" crossorigin=anonymous></script><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bulma-slider.min.js integrity="sha256-22jr4VSiVZeRPFY18xUA/noy5aIF+5qYyWQtDC3kfZ4=" crossorigin=anonymous></script><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bulma-slider.min.css integrity="sha256-+dlSYn04i4uiZ+E4jWkjJ0z55i51jUWcWgRfOTPp3Io=" crossorigin=anonymous><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bulma-carousel.min.css integrity="sha256-nVhrDZU/ne3I/z6LXWEbyUOEmv79sPGKEbsP7SWLkHI=" crossorigin=anonymous><link rel=stylesheet href=static/index.css><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/clipboard.min.js integrity="sha256-4XodgW4TwIJuDtf+v6vDJ39FVxI0veC/kSCCmnFp7ck=" crossorigin=anonymous></script><script defer src=https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/js/all.min.js integrity="sha256-gSqw5G+Gss6YqyQlqyIkuQ0IRZUqGsDVq9c0tiF+mL8=" crossorigin=anonymous></script><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/fontawesome.min.css integrity="sha256-BYjRZhSY2ARUPcFTf5eEh3qWK58O88TM7nZet/JUNhE=" crossorigin=anonymous><link rel=stylesheet href=https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css><link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel=stylesheet><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/default.min.css><script src=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js></script><script src=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/languages/bash.min.js></script><script defer src=https://cloud.umami.is/script.js data-website-id=c18ebe0b-2d3c-48ff-9cef-62c34bbadb04></script></head><body><section class=hero id=title><div class=hero-body><div class="container is-max-desktop" style=max-width:1200px><div class="columns is-centered"><div class="column has-text-centered"><h1 class="title is-1 publication-title">ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution</h1><div class="is-size-5 publication-authors"><span class=author-block><a href=https://henry-yeh.github.io/ target=_blank>Haoran Ye</a><sup>1</sup>,</span><span class=author-block><a href=https://github.com/Furffico target=_blank>Jiarui Wang</a><sup>2</sup>,</span><span class=author-block><a href=https://zhiguangcaosg.github.io/ target=_blank>Zhiguang Cao</a><sup>3</sup>,</span><span class=author-block><a href=https://fedebotu.github.io/ target=_blank>Federico Berto</a><sup>4</sup>,</span><span class=author-block><a href=https://cbhua.github.io/ target=_blank>Chuanbo Hua</a><sup>4</sup>,</span><span class=author-block><a href=https://sites.google.com/view/haeyeon-rachel-kim target=_blank>Haeyeon Kim</a><sup>4</sup>,</span><span class=author-block><a href=https://pure.kaist.ac.kr/en/persons/jinkyoo-park target=_blank>Jinkyoo Park</a><sup>4</sup>,</span><span class=author-block><a href=https://www.cis.pku.edu.cn/info/1362/2256.htm target=_blank>Guojie Song</a><sup>1,5</sup></span></div><div class="is-size-6 publication-authors"><span class=author-block style=margin-right:.5em><sup>1 </sup>National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University</span><br><span class=author-block style=margin-right:.5em><sup>2 </sup>Southeast University</span><span class=author-block style=margin-right:.5em><sup>3 </sup>Singapore Management University</span><span class=author-block style=margin-right:.5em><sup>4 </sup>KAIST</span><span class=author-block style=margin-right:.5em><sup>5 </sup>PKU-Wuhan Institute for Artificial Intelligence</span><span class=author-block style=margin-right:.5em><sup>† </sup><a href=https://github.com/ai4co target=_blank>AI4CO</a></span><br><b class="author-block is-size-5" style="padding:1ex 0">NeurIPS 2024</b><span class=eql-cntrb><br><sup>†</sup>Work made with contributions from the AI4CO open research community.</span></div><div class="column has-text-centered"><div class=publication-links><span class=link-block><a href=https://arxiv.org/pdf/2402.01145 target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="fas fa-file-pdf"></i></span><span>Paper</span></a></span><span class=link-block><a href=https://arxiv.org/abs/2402.01145 target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="ai ai-arxiv"></i></span><span>arXiv</span></a></span><span class=link-block><a href=https://github.com/ai4co/reevo target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="fab fa-github"></i></span><span>Code</span></a></span><span class=link-block><a href=https://join.slack.com/t/rl4co/shared_invite/zt-1ytz2c1v4-0IkQ8NQH4TRXIX8PrRmDhQ target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="fab fa-slack"></i></span><span>Slack</span></a></span></div></div><div class="column has-text-centered is-size-5"><b>Give ReEvo 5 minutes, and get a state-of-the-art algorithm in return!</b></div></div></div></div></div></section><section class="section hero is-light red-link" id=abstract><div class="container is-max-desktop"><div class="columns is-centered has-text-centered"><div class="column is-four-fifths"><h2 class="title is-3">Abstract</h2><div class="content has-text-justified"><p>The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present <b>Reflective Evolution</b> (<span style=font-family:monospace>ReEvo</span>), a novel integration of evolutionary search for efficiently exploring the heuristic space, and LLM reflections to provide verbal gradients within the space. Across five heterogeneous algorithmic types, six different COPs, and both white-box and black-box views of COPs, <span style=font-family:monospace>ReEvo</span> yields state-of-the-art and competitive meta-heuristics, evolutionary algorithms, heuristics, and neural solvers, while being more sample-efficient than prior LHHs. Our code is available: <a href=https://github.com/ai4co/reevo target=_blank>https://github.com/ai4co/reevo</a>. </p></div></div></div></div></section><section class="hero is-small" id=carousel><div class=hero-body><div class=container><div id=results-carousel class="carousel results-carousel"><div class=item><img src=static/images/reevo.png alt="<span style=" font-family:monospace&quot;>ReEvo</span> pipeline." /> <h2 class="subtitle has-text-centered" style=margin-top:1ex><span style=font-family:monospace>ReEvo</span> pipeline. </h2></div><div class=item><img src=static/images/show_reflec.png alt="Examples of reflections for black-box TSP."><h2 class="subtitle has-text-centered" style=margin-top:1ex> Examples of reflections for black-box TSP. </h2></div></div></div></div></section><section class="section hero is-light red-link" id=usage><div class="container is-max-desktop content" style=width:100%><h2 class=title>Usage 🔑</h2><h3>Get Started</h3><div class=code-container style=background-color:white><pre><code class="code-content language-bash" style=background-color:white id=code-content-1>$ git clone [email protected]:ai4co/reevo.git # download reevo code
+<!DOCTYPE html><html><head><meta charset=utf-8><meta name=viewport content="width=device-width, initial-scale=1"><title>[NeurIPS 2024] ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution</title><link rel=icon type=image/svg href=static/icon.svg><link rel=preconnect href=https://cdn.jsdelivr.net crossorigin><link rel=preconnect href=https://fonts.googleapis.com crossorigin><link rel=preconnect href=https://cdnjs.cloudflare.com crossorigin><link rel=preload href=static/index.css as=style><link rel=preload href=static/images/reevo.png as=image><link rel=preload href=static/images/show_reflec.png as=image><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/css/bulma.min.css integrity="sha256-WLKGWSIJYerRN8tbNGtXWVYnUM5wMJTXD8eG4NtGcDM=" crossorigin=anonymous><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bulma-carousel.min.js integrity="sha256-qKR77yzVBkDYKuoSg2BpAIbMtmF8aFjuTHFVnIjkpzI=" crossorigin=anonymous></script><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/js/bulma-slider.min.js integrity="sha256-22jr4VSiVZeRPFY18xUA/noy5aIF+5qYyWQtDC3kfZ4=" crossorigin=anonymous></script><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bulma-slider.min.css integrity="sha256-+dlSYn04i4uiZ+E4jWkjJ0z55i51jUWcWgRfOTPp3Io=" crossorigin=anonymous><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/[email protected]/dist/css/bulma-carousel.min.css integrity="sha256-nVhrDZU/ne3I/z6LXWEbyUOEmv79sPGKEbsP7SWLkHI=" crossorigin=anonymous><link rel=stylesheet href=static/index.css><script src=https://cdn.jsdelivr.net/npm/[email protected]/dist/clipboard.min.js integrity="sha256-4XodgW4TwIJuDtf+v6vDJ39FVxI0veC/kSCCmnFp7ck=" crossorigin=anonymous></script><script defer src=https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/js/all.min.js integrity="sha256-gSqw5G+Gss6YqyQlqyIkuQ0IRZUqGsDVq9c0tiF+mL8=" crossorigin=anonymous></script><link rel=stylesheet href=https://cdn.jsdelivr.net/npm/@fortawesome/[email protected]/css/fontawesome.min.css integrity="sha256-BYjRZhSY2ARUPcFTf5eEh3qWK58O88TM7nZet/JUNhE=" crossorigin=anonymous><link rel=stylesheet href=https://cdn.jsdelivr.net/gh/jpswalsh/academicons@1/css/academicons.min.css><link href="https://fonts.googleapis.com/css?family=Google+Sans|Noto+Sans|Castoro" rel=stylesheet><link rel=stylesheet href=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/styles/default.min.css><script src=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/highlight.min.js></script><script src=https://cdnjs.cloudflare.com/ajax/libs/highlight.js/11.9.0/languages/bash.min.js></script><script defer src=https://cloud.umami.is/script.js data-website-id=c18ebe0b-2d3c-48ff-9cef-62c34bbadb04></script></head><body><section class=hero id=title><div class=hero-body><div class="container is-max-desktop" style=max-width:1200px><div class="columns is-centered"><div class="column has-text-centered"><h1 class="title is-1 publication-title">ReEvo: Large Language Models as Hyper-Heuristics with Reflective Evolution</h1><div class="is-size-5 publication-authors"><span class=author-block><a href=https://henry-yeh.github.io/ target=_blank>Haoran Ye</a><sup>1</sup>,</span><span class=author-block><a href=https://github.com/Furffico target=_blank>Jiarui Wang</a><sup>2</sup>,</span><span class=author-block><a href=https://zhiguangcaosg.github.io/ target=_blank>Zhiguang Cao</a><sup>3</sup>,</span><span class=author-block><a href=https://fedebotu.github.io/ target=_blank>Federico Berto</a><sup>4</sup>,</span><span class=author-block><a href=https://cbhua.github.io/ target=_blank>Chuanbo Hua</a><sup>4</sup>,</span><span class=author-block><a href=https://sites.google.com/view/haeyeon-rachel-kim target=_blank>Haeyeon Kim</a><sup>4</sup>,</span><span class=author-block><a href=https://pure.kaist.ac.kr/en/persons/jinkyoo-park target=_blank>Jinkyoo Park</a><sup>4</sup>,</span><span class=author-block><a href=https://www.cis.pku.edu.cn/info/1362/2256.htm target=_blank>Guojie Song</a><sup>1,5</sup></span></div><div class="is-size-6 publication-authors"><span class=author-block style=margin-right:.5em><sup>1 </sup>National Key Laboratory of General Artificial Intelligence, School of Intelligence Science and Technology, Peking University</span><br><span class=author-block style=margin-right:.5em><sup>2 </sup>Southeast University</span><span class=author-block style=margin-right:.5em><sup>3 </sup>Singapore Management University</span><span class=author-block style=margin-right:.5em><sup>4 </sup>KAIST</span><span class=author-block style=margin-right:.5em><sup>5 </sup>PKU-Wuhan Institute for Artificial Intelligence</span><span class=author-block style=margin-right:.5em><sup>† </sup><a href=https://github.com/ai4co target=_blank>AI4CO</a></span><br><b class="author-block is-size-5" style="padding:1ex 0">NeurIPS 2024</b><span class=eql-cntrb><br><sup>†</sup>Work made with contributions from the AI4CO open research community.</span></div><div class="column has-text-centered"><div class=publication-links><span class=link-block><a href=https://arxiv.org/pdf/2402.01145 target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="fas fa-file-pdf"></i></span><span>Paper</span></a></span><span class=link-block><a href=https://arxiv.org/abs/2402.01145 target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="ai ai-arxiv"></i></span><span>arXiv</span></a></span><span class=link-block><a href=https://github.com/ai4co/reevo target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="fab fa-github"></i></span><span>Code</span></a></span><span class=link-block><a href=https://join.slack.com/t/rl4co/shared_invite/zt-1ytz2c1v4-0IkQ8NQH4TRXIX8PrRmDhQ target=_blank class="external-link button is-normal is-rounded is-dark"><span class=icon><i class="fab fa-slack"></i></span><span>Slack</span></a></span></div></div><div class="column has-text-centered is-size-5"><b>Give ReEvo 5 minutes, and get a state-of-the-art algorithm in return!</b></div></div></div></div></div></section><section class="section hero is-light red-link" id=abstract><div class="container is-max-desktop"><div class="columns is-centered has-text-centered"><div class="column is-four-fifths"><h2 class="title is-3">Abstract</h2><div class="content has-text-justified"><p>The omnipresence of NP-hard combinatorial optimization problems (COPs) compels domain experts to engage in trial-and-error heuristic design. The long-standing endeavor of design automation has gained new momentum with the rise of large language models (LLMs). This paper introduces Language Hyper-Heuristics (LHHs), an emerging variant of Hyper-Heuristics that leverages LLMs for heuristic generation, featuring minimal manual intervention and open-ended heuristic spaces. To empower LHHs, we present <b>Reflective Evolution</b> (<span style=font-family:monospace>ReEvo</span>), a novel integration of evolutionary search for efficiently exploring the heuristic space, and LLM reflections to provide verbal gradients within the space. Across five heterogeneous algorithmic types, six different COPs, and both white-box and black-box views of COPs, <span style=font-family:monospace>ReEvo</span> yields state-of-the-art and competitive meta-heuristics, evolutionary algorithms, heuristics, and neural solvers, while being more sample-efficient than prior LHHs. </p></div></div></div></div></section><section class="hero is-small" id=carousel><div class=hero-body><div class=container><div id=results-carousel class="carousel results-carousel"><div class=item><img src=static/images/reevo.png alt="<span style=" font-family:monospace&quot;>ReEvo</span> pipeline." /> <h2 class="subtitle has-text-centered" style=margin-top:1ex><span style=font-family:monospace>ReEvo</span> pipeline. </h2></div><div class=item><img src=static/images/show_reflec.png alt="Examples of reflections for black-box TSP."><h2 class="subtitle has-text-centered" style=margin-top:1ex> Examples of reflections for black-box TSP. </h2></div></div></div></div></section><section class="section hero is-light red-link" id=usage><div class="container is-max-desktop content" style=width:100%><h2 class=title>Usage 🔑</h2><h3>Get Started</h3><div class=code-container style=background-color:white><pre><code class="code-content language-bash" style=background-color:white id=code-content-1>$ git clone [email protected]:ai4co/reevo.git # download reevo code
 $ cd reevo
 $ python3 -m venv ./venv            # [optional] create virtual environment
 $ source ./venv/bin/activate        # [optional] activate virtual environment