[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119836-en":3,"doc-seo-119836-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},119836,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","A machine learning framework for neighbor generation in metaheuristic search","A machine learning framework integrates learning into metaheuristics for combinatorial optimization. The approach defines an efficient neighborhood structure by transforming a selected subset of variables from the current solution, then generates promising neighbors through a variable subset choice that is likely to contain objective descent. Variable selection is learned as a classification problem using structural information from problem characteristics and high-quality solutions. Validation covers Tabu Search for Wireless Network Optimization and Large Neighborhood Search for Mixed-Integer Programs, showing a favorable exploration–exploitation trade-off.","Titre:  A machine learning framework for neighbor generation in  \nTitle:  metaheuristic search  \nAuteurs:   \n Defeng Liu, Vincent Perreault, Alain Hertz, & Andrea Lodi  \n Authors:   Date:  2023   Type:  Article de revue / Article   \nRéférence:  Liu, D. , Perreault, V., Hertz, A. , & Lodi, A. (2023) . A machine learning framework for neighbor generation in metaheuristic search. Frontiers in Applied Mathematics  \nCitation:   \n  and Statistics, 9, 15 pages. [https://doi.org/10.3389/fams.2023.1128181](https://doi.org/10.3389/fams.2023.1128181)   \n| Document en libre accès dans PolyPublie\u003Cbr>Open Access document in PolyPublie\u003Cbr> |  |  |\n| --- | --- | --- |\n| URL de PolyPublie:\u003Cbr>PolyPublie URL: | [https://publications.polymtl.ca/54613/](https://publications.polymtl.ca/54613/)\u003Cbr> |  |\n| Version:  | Révisé par les pairs / Refereed     |  |\n| Conditions d’utilisation:\u003Cbr>Terms of Use:\u003Cbr> | CC BY |  |\n\n\n| Document publié chez l’éditeur officiel\u003Cbr>Document issued by the official publisher\u003Cbr> |  |  |\n| --- | --- | --- |\n| Titre de la revue: |  | Frontiers in Applied Mathematics and Statistics (vol. 9) |\n|  | Journal Title: |  |\n| Maison d’édition:\u003Cbr>Publisher:\u003Cbr> |  | Frontiers media sa |\n| URL officiel:\u003Cbr>Official URL: |  | [https://doi.org/10.3389/fams.2023.1128181](https://doi.org/10.3389/fams.2023.1128181) |\n| Mention légale:\u003Cbr>Legal notice: |  | \u003Cbr>© 2023 Liu, Perreault, Hertz and Lodi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright wner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.   |\n\nCe fichier a été téléchargé à partir de PolyPublie, le dépôt institutionnel de Polytechnique Montréal  \nThis file has been downloaded from PolyPublie, the institutional repository of Polytechnique Montréal  \n[https://publications.polymtl.ca](https://publications.polymtl.ca)  \nTYPE Original Research PUBLISHED 26 July 2023  \nDOI 10. 3389/fams.2023.1128181  \nOPEN ACCESS  \nEDITED BY  \nAndreas M. Tillmann,  \nTechnical University of Braunschweig, Germany  \nREVIEWED BY  \nChristoph Hansknecht,  \nTechnical University of Braunschweig, Germany Stephan Westphal,  \nClausthal University of Technology, Germany  \n*CORRESPONDENCE  \nAndrea Lodi  \n [andrea.lodi@cornell.edu](andrea.lodi@cornell.edu)  \nRECEIVED 20 December 2022  \nACCEPTED 10 July 2023  \nPUBLISHED 26 July 2023  \nCITATION  \nLiu D, Perreault V, Hertz A and Lodi A (2023) A machine learning framework for neighbor generation in metaheuristic search.  \nFront. Appl. Math. Stat. 9:1128181 .  \ndoi: 10.3389/fams.2023.1128181  \nCOPYRIGHT  \n© 2023 Liu, Perreault, Hertz and Lodi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nA machine learning framework for neighbor generation in metaheuristic search  \nDefeng Liu1 , Vincent Perreault1 , Alain Hertz1 and Andrea Lodi2*  \n1 Department of Mathematics and Industrial Engineering, Polytechnique Montreal, Montreal, QC, Canada, 2Jacobs Technion-Cornell Institute, Cornell Tech and Technion—IIT, New York, NY, United States  \nThis paper presents a methodology for integrating machine learning techniques into metaheuristics for solving combinatorial optimization problems. Namely, we propose a general machine learning framework for neighbor generation in metaheuristic search. We first define an e􀀈cient neighborhood structure const","cbCaifIWWJ7PNI7Q","https://ap.wps.com/l/cbCaifIWWJ7PNI7Q","pdf",1296382,1,16,"English","en",105,"# Introduction\n## Problem background and motivation\n## Proposed learning framework for neighbor generation\n## Learning variable selection strategy\n## Experimental validation on applications\n## Results and discussion","[{\"question\":\"What does the framework do in metaheuristic search?\",\"answer\":\"It integrates machine learning into metaheuristics by learning how to select variables for generating promising neighbors in the solution space.\"},{\"question\":\"How is the neighborhood structure constructed?\",\"answer\":\"The method constructs an efficient neighborhood by applying a transformation to a chosen subset of variables from the current solution.\"},{\"question\":\"Which optimization problems are used to validate the approach?\",\"answer\":\"It is validated on Tabu Search for Wireless Network Optimization and on Large Neighborhood Search for Mixed-Integer Programs.\"}]","A machine learning framework for neighbor generation in metaheuristic search | PDF",1785726556,40,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-machine-learning-framework-for-neighbor-generation-in-metaheuristic-search","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-machine-learning-framework-for-neighbor-generation-in-metaheuristic-search/119836/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What does the framework do in metaheuristic search?","Question",{"text":75,"@type":76},"It integrates machine learning into metaheuristics by learning how to select variables for generating promising neighbors in the solution space.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the neighborhood structure constructed?",{"text":80,"@type":76},"The method constructs an efficient neighborhood by applying a transformation to a chosen subset of variables from the current solution.",{"name":82,"@type":73,"acceptedAnswer":83},"Which optimization problems are used to validate the approach?",{"text":84,"@type":76},"It is validated on Tabu Search for Wireless Network Optimization and on Large Neighborhood Search for Mixed-Integer Programs.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,119,122,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":29,"slug":118},7,"Healthcare","healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]