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

D. Duzanec, Z. Kovačić
{"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}
引用次数: 5

摘要

虽然遗传算法的设计方法已经建立,但确定最优遗传算法参数的一般表达式仍然缺乏。还有一个问题是,找到的解决方案可能不准确。本文描述了一个基于选择向量的优化问题的遗传算法性能分析,从而得出了有用的遗传算法参数选择准则。本文还介绍了一种通过在优化过程中逐步压缩候选解空间来提高互补微遗传算法精度的新方法。在13自由度触手机器人模型上进行了改进μGA算法的测试,性能分析表明,在不影响算法持续时间的情况下,算法精度得到了显著提高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信