[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-159053-105":59,"doc-detail-159053-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","m-paes-a-memetic-algorithm-for-multiobjective-optimization","M-PAES - A Memetic Algorithm for Multiobjective Optimization","","A memetic algorithm for tackling multiobjective optimization problems is presented. The method combines the proven local search strategy of the Pareto archived evolution strategy (PAES) with population-based recombination. Verification is performed on a set of multiobjective 0/1 knapsack instances, comparing the new algorithm to the (1+1)-PAES local searcher and to the strength Pareto evolutionary algorithm (SPEA) of Zitzler and Thiele.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/m-paes-a-memetic-algorithm-for-multiobjective-optimization/159053/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/m-paes-a-memetic-algorithm-for-multiobjective-optimization/159053.png","ImageObject",300,407,{"name":92,"@type":93},"Riley West","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-06","2026-08-29",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",14,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What core idea does M-PAES use for multiobjective optimization?","Question",{"text":113,"@type":114},"It integrates PAES-style local search with population and recombination to enhance performance on multiobjective problems.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How was the algorithm verified?",{"text":118,"@type":114},"It was tested on a set of multiobjective 0/1 knapsack problems.",{"name":120,"@type":111,"acceptedAnswer":121},"Which algorithms are compared against M-PAES?",{"text":122,"@type":114},"Comparisons include the (1+1)-PAES local searcher and SPEA by Zitzler and Thiele.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},159053,1788011400,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":39,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":46},1099523885074,"https://ap-avatar.wpscdn.com/davatar_9964176cb1d06d4a9deccf72a44ae3dc","# M-PAES:A Memetic Algorithm for Multiobjective Optimization\n\nJoshua D.Knowles and David W.Corne  \nSchool of Computer Science,Cybermetics and Electronic EngineeringUniversity of Reading,Reading RG66AY,UK  \nj.d.knowles@reading.ac.uk,d.w.corne@reading.ac.ukhttp://www.rdg.ac.uk/~ssr97jdk  \nAbstract-  \nA memetic algorithm for tackling multiobjective op-timization problems is presented.The algorithm em-ploys the proven local search strategy used in the Paretoarchived evolution strategy(PAES)and combines it withthe use of a population and recombination.Verificationof the new algorithm is carried out by testing it on aset of multiobjective 0/1 knapsack problems.On eachproblem instance,comparison is made between the newmemetic algorithm,the(1+1)-PAES local searcher,andthe strength Pareto evolutionary algorithm(SPEA)of Zit-zler and Thiele.  \n## 1 Introduction\n\nIn recent years,genetic algorithms(GAs)have been appliedmore and more to multiobjective problems.For a compre-hensive overview,see [2].Undoubtedly,as an extremely gen-eral metaheuristic,GAs are well qualified to tackle problemsof a great variety.This asset,coupled with the possessionof a population,seems to make them particularly attractivefor use in multiobjective problems,where a number of so-lutions approximating the Pareto front are required.Indeed,changing a generic GA into a multiobjective GA(MOGA)is a relatively simple task:A selection scheme that operateswith solutions possessing a vector of objective scores is theonly extra requirement,although some means of maintain-ing diversity in the population is also often desirable.Thefirst,pioneering work in the field was Schaffer's vector eval-uated GA(VEGA)[25],which alternately optimized each ofthe different objectives.Later,Goldberg [9],suggested anelegant method of ranking a population of solutions,basedon their mutual dominance relations.This was implementedin an algorithm,NSGA,by Srinivas and Deb [27]in 1994.Since then,Pareto methods like these have been very popular—due again to their very general applicability,and their lackof assumptions about the decision maker—and several othermethods of assigning fitness based on some form of Paretoranking have been devised,e.g.[4,10].More recently,elitismhas been shown to improve the performance of multiobjec-tive GAs(for example see [21]),and a very elegant methodof exploiting co-evolution to perform fitness assignment in anelitist GA was put forward by Zitzler and Thiele [30,31,32].The latter has been compared to some of the most popularMOGAs,on a range of problems and test functions,with verypositive results.Some theoretical justification for the use ofevolutionary algorithms in multiobjective optimization,in theform of convergence proofs,has also been provided [23,24].  \nAlmost in parallel to the development of MOGAs,there  \n0-7803-6375-2/00/$10.00 C2000 IEEE.  \nhas been a growing research effort in the use of metaheuristicswithin the field of multiple criteria decision making(MCDM)—a branch of operations research.Algorithms based onboth tabu search and simulated annealing have been put for-ward [3,7,8,22,26,28].Most of these algorithms do nothave a population but store the nondominated solutions dis-covered during a local search process.Rather than usingPareto ranking,weighted metrics are used to aggregate theobjectives into a single score to be used in the acceptancefunction [29].Some researchers argue that the use of suchscalarizing vectors naturally allows the preferences of the de-cision maker to be used to guide the direction(s)of the searchtowards the region(s)of interest(see for example [3]).Thismay be true,but the use of purely random utility functions inthe absence of such preference information,as used in manyalgorithms,seems unsatisfactory.  \nWhether the algorithms devised and investigated in theMCDM field are more or less effective than MOGAs remainsan open question:Very few studies that attempt to directlymeasure and compare the performance of MOGAs with al-gorithm","cbCaigDFA7IFQc2s","https://ap.wps.com/l/cbCaigDFA7IFQc2s","pdf",732954,"English","# 1 Introduction\n# 2 Related Work\n## 2.1 Multiobjective Genetic Algorithms\n## 2.2 MCDM Metaheuristics","[{\"question\":\"What core idea does M-PAES use for multiobjective optimization?\",\"answer\":\"It integrates PAES-style local search with population and recombination to enhance performance on multiobjective problems.\"},{\"question\":\"How was the algorithm verified?\",\"answer\":\"It was tested on a set of multiobjective 0/1 knapsack problems.\"},{\"question\":\"Which algorithms are compared against M-PAES?\",\"answer\":\"Comparisons include the (1+1)-PAES local searcher and SPEA by Zitzler and Thiele.\"}]","M-PAES - A Memetic Algorithm for Multiobjective Optimization | PDF"]