{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/novel-methods-for-enhancing-the-performance","title":"Novel Methods for Enhancing the Performance of Genetic Algorithms","arxiv_id":"1801.02827","date":"2018-01-09","proceeding":null,"authors":["Esra'a O Alkafaween"],"abstract":"In this thesis we propose new methods for crossover operator namely: cut on\nworst gene (COWGC), cut on worst L+R gene (COWLRGC) and Collision Crossovers.\nAnd also we propose several types of mutation operator such as: worst gene with\nrandom gene mutation (WGWRGM) , worst LR gene with random gene mutation\n(WLRGWRGM), worst gene with worst gene mutation (WGWWGM), worst gene with\nnearest neighbour mutation (WGWNNM), worst gene with the worst around the\nnearest neighbour mutation (WGWWNNM), worst gene inserted beside nearest\nneighbour mutation (WGIBNNM), random gene inserted beside nearest neighbour\nmutation (RGIBNNM), Swap worst gene locally mutation (SWGLM), Insert best\nrandom gene before worst gene mutation (IBRGBWGM) and Insert best random gene\nbefore random gene mutation (IBRGBRGM). In addition to proposing four selection\nstrategies, namely: select any crossover (SAC), select any mutation (SAM),\nselect best crossover (SBC) and select best mutation (SBM). The first two are\nbased on selection of the best crossover and mutation operator respectively,\nand the other two strategies randomly select any operator. So we investigate\nthe use of more than one crossover/mutation operator (based on the proposed\nstrategies) to enhance the performance of genetic algorithms. Our experiments,\nconducted on several Travelling Salesman Problems (TSP), show the superiority\nof some of the proposed methods in crossover and mutation over some of the\nwell-known crossover and mutation operators described in the literature. In\naddition, using any of the four strategies (SAC, SAM, SBC and SBM), found to be\nbetter than using one crossover/mutation operator in general, because those\nallow the GA to avoid local optima, or the so-called premature convergence.\nKeywords: GAs, Collision crossover, Multi crossovers, Multi mutations, TSP.","url_abs":"http://arxiv.org/abs/1801.02827v3","url_pdf":"http://arxiv.org/pdf/1801.02827v3.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"novel-methods-for-enhancing-the-performance","repo_url":"https://github.com/GitNickProgramming/FinalProject_CS3120","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null},{"paper_slug":"novel-methods-for-enhancing-the-performance","repo_url":"https://github.com/diogomatoschaves/geneal","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null}],"tasks":[],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}