{"title":"基于概率模型的多种群多样性维持进化优化","authors":"Takayuki Higo, K. Takadama","doi":"10.2197/IPSJDC.4.268","DOIUrl":null,"url":null,"abstract":"This paper proposes a novel method, Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM)such as estimation of distribution algorithms and cross entropy methods. In HIS, multiple populations are maintained simultaneously such that they have different diversities, and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM.","PeriodicalId":432390,"journal":{"name":"Ipsj Digital Courier","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2008-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models\",\"authors\":\"Takayuki Higo, K. Takadama\",\"doi\":\"10.2197/IPSJDC.4.268\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper proposes a novel method, Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM)such as estimation of distribution algorithms and cross entropy methods. In HIS, multiple populations are maintained simultaneously such that they have different diversities, and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM.\",\"PeriodicalId\":432390,\"journal\":{\"name\":\"Ipsj Digital Courier\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2008-03-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Ipsj Digital Courier\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.2197/IPSJDC.4.268\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Ipsj Digital Courier","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2197/IPSJDC.4.268","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Maintaining Multiple Populations with Different Diversities for Evolutionary Optimization Based on Probability Models
This paper proposes a novel method, Hierarchical Importance Sampling (HIS) that can be used instead of population convergence in evolutionary optimization based on probability models (EOPM)such as estimation of distribution algorithms and cross entropy methods. In HIS, multiple populations are maintained simultaneously such that they have different diversities, and the probability model of one population is built through importance sampling by mixing with the other populations. This mechanism can allow populations to escape from local optima. Experimental comparisons reveal that HIS outperforms general EOPM.