{"title":"基于性能分析的遗传算法参数选择和逐步压缩解空间提高μGA精度","authors":"D. Duzanec, Z. Kovačić","doi":"10.1109/ICIT.2009.4939616","DOIUrl":null,"url":null,"abstract":"Although methods for design of genetic algorithms (GA) are well established, general expressions for determination of optimal GA parameters are still missing. There is also a problem of possible inaccuracy of a found solution. This paper describes a GA performance analysis for a selected vector-based optimization problem that has led to useful GA parameter selection criteria. The paper also describes a new method for increasing the precision of a complementary micro genetic algorithm (μGA) by enforcing gradual contraction of the space of candidate solutions during optimization. The enhanced μGA has been tested on the model of a 13-DOF tentacle robot, and the performance analysis showed significant improvement of accuracy without affecting the duration of the algorithm.","PeriodicalId":405687,"journal":{"name":"2009 IEEE International Conference on Industrial Technology","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2009-02-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"5","resultStr":"{\"title\":\"Performance analysis-based GA parameter selection and increase of μGA accuracy by gradual contraction of solution space\",\"authors\":\"D. Duzanec, Z. Kovačić\",\"doi\":\"10.1109/ICIT.2009.4939616\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Although methods for design of genetic algorithms (GA) are well established, general expressions for determination of optimal GA parameters are still missing. There is also a problem of possible inaccuracy of a found solution. This paper describes a GA performance analysis for a selected vector-based optimization problem that has led to useful GA parameter selection criteria. The paper also describes a new method for increasing the precision of a complementary micro genetic algorithm (μGA) by enforcing gradual contraction of the space of candidate solutions during optimization. The enhanced μGA has been tested on the model of a 13-DOF tentacle robot, and the performance analysis showed significant improvement of accuracy without affecting the duration of the algorithm.\",\"PeriodicalId\":405687,\"journal\":{\"name\":\"2009 IEEE International Conference on Industrial Technology\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2009-02-10\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"5\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2009 IEEE International Conference on Industrial Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIT.2009.4939616\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2009 IEEE International Conference on Industrial Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIT.2009.4939616","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Performance analysis-based GA parameter selection and increase of μGA accuracy by gradual contraction of solution space
Although methods for design of genetic algorithms (GA) are well established, general expressions for determination of optimal GA parameters are still missing. There is also a problem of possible inaccuracy of a found solution. This paper describes a GA performance analysis for a selected vector-based optimization problem that has led to useful GA parameter selection criteria. The paper also describes a new method for increasing the precision of a complementary micro genetic algorithm (μGA) by enforcing gradual contraction of the space of candidate solutions during optimization. The enhanced μGA has been tested on the model of a 13-DOF tentacle robot, and the performance analysis showed significant improvement of accuracy without affecting the duration of the algorithm.