{"title":"排序问题的鲁棒分布式遗传算法","authors":"Anup Kumar, A. Srivastava, A. Singru, R. K. Ghosh","doi":"10.1109/HPDC.1996.546195","DOIUrl":null,"url":null,"abstract":"The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and does not require the use of any specialized, expensive hardware. Fault tolerance has also been provided by dynamic reconfiguration of the distributed system in the event of a process or machine failure. The effectiveness of using a simple crossover scheme with Distributed GA is demonstrated by solving three variations of the Traveling Salesman Problem (TSP).","PeriodicalId":267002,"journal":{"name":"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"1996-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"8","resultStr":"{\"title\":\"Robust and distributed genetic algorithm for ordering problems\",\"authors\":\"Anup Kumar, A. Srivastava, A. Singru, R. K. Ghosh\",\"doi\":\"10.1109/HPDC.1996.546195\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and does not require the use of any specialized, expensive hardware. Fault tolerance has also been provided by dynamic reconfiguration of the distributed system in the event of a process or machine failure. The effectiveness of using a simple crossover scheme with Distributed GA is demonstrated by solving three variations of the Traveling Salesman Problem (TSP).\",\"PeriodicalId\":267002,\"journal\":{\"name\":\"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1996-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"8\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/HPDC.1996.546195\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of 5th IEEE International Symposium on High Performance Distributed Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPDC.1996.546195","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Robust and distributed genetic algorithm for ordering problems
The paper presents a distributed genetic algorithm implementation for obtaining good quality consistent results for different ordering problems. Most importantly, the solution found by the proposed Distributed GA is not only of high quality but also robust and does not require fine tuning of the probabilities of crossover and mutation. In addition, implementation of the Distributed GA is simple and does not require the use of any specialized, expensive hardware. Fault tolerance has also been provided by dynamic reconfiguration of the distributed system in the event of a process or machine failure. The effectiveness of using a simple crossover scheme with Distributed GA is demonstrated by solving three variations of the Traveling Salesman Problem (TSP).