{"title":"遗传算法参数对结构优化设计搜索的影响","authors":"Z. E. Maskaoui, S. Jalal, L. Bousshine","doi":"10.9790/1684-140305124130","DOIUrl":null,"url":null,"abstract":"This paper investigates the effects of genetic algorithm parameters on the performance of optimum structural search. The most significant of these parameters can be grouped according to their biologicallyinspired functions: population size, initial population, and crossover and mutation operators. However, since the genetic algorithms use a random search the numerical results presented in this paper show the extent to which the quality of solution depends on the choice of these parameters.","PeriodicalId":14565,"journal":{"name":"IOSR Journal of Mechanical and Civil Engineering","volume":"4 1","pages":"124-130"},"PeriodicalIF":0.0000,"publicationDate":"2017-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Genetic Algorithm Parameters Effect on the Optimal Structural Design Search\",\"authors\":\"Z. E. Maskaoui, S. Jalal, L. Bousshine\",\"doi\":\"10.9790/1684-140305124130\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper investigates the effects of genetic algorithm parameters on the performance of optimum structural search. The most significant of these parameters can be grouped according to their biologicallyinspired functions: population size, initial population, and crossover and mutation operators. However, since the genetic algorithms use a random search the numerical results presented in this paper show the extent to which the quality of solution depends on the choice of these parameters.\",\"PeriodicalId\":14565,\"journal\":{\"name\":\"IOSR Journal of Mechanical and Civil Engineering\",\"volume\":\"4 1\",\"pages\":\"124-130\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IOSR Journal of Mechanical and Civil Engineering\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.9790/1684-140305124130\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IOSR Journal of Mechanical and Civil Engineering","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.9790/1684-140305124130","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Genetic Algorithm Parameters Effect on the Optimal Structural Design Search
This paper investigates the effects of genetic algorithm parameters on the performance of optimum structural search. The most significant of these parameters can be grouped according to their biologicallyinspired functions: population size, initial population, and crossover and mutation operators. However, since the genetic algorithms use a random search the numerical results presented in this paper show the extent to which the quality of solution depends on the choice of these parameters.