Y. Matsumura, Ayumu Kobayashi, Kiyotaka Sugiyama, T. Pataky, T. Yasuda, K. Ohkura, Bill Sellers
{"title":"A (μ, λ)进化与粒子群混合算法,并应用于恐龙步态优化","authors":"Y. Matsumura, Ayumu Kobayashi, Kiyotaka Sugiyama, T. Pataky, T. Yasuda, K. Ohkura, Bill Sellers","doi":"10.1109/IWCIA.2013.6624791","DOIUrl":null,"url":null,"abstract":"A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase.","PeriodicalId":257474,"journal":{"name":"2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization\",\"authors\":\"Y. Matsumura, Ayumu Kobayashi, Kiyotaka Sugiyama, T. Pataky, T. Yasuda, K. Ohkura, Bill Sellers\",\"doi\":\"10.1109/IWCIA.2013.6624791\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase.\",\"PeriodicalId\":257474,\"journal\":{\"name\":\"2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)\",\"volume\":\"1 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-07-13\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IWCIA.2013.6624791\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE 6th International Workshop on Computational Intelligence and Applications (IWCIA)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IWCIA.2013.6624791","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
A (μ, λ) evolutionary and particle swarm hybrid algorithm, with an application to dinosaur gait optimization
A hybrid evolutionary algorithm based on (μ, λ) evolutionary algorithms and particle swarm optimization is proposed for the numerical optimization problems. In order to find out the performance of the hybrid, the computer experiment is tested on dinosaur's gait generation problem. Experimental results show that hybrid optimization finds maximum fitness and is faster in the first phase.