{"title":"进化机器人的可进化性:基因型-表型图谱的进化","authors":"L. König, H. Schmeck","doi":"10.1109/SASO.2010.27","DOIUrl":null,"url":null,"abstract":"A completely evolvable genotype-phenotype mapping (ceGPM) is studied with respect to its capability of improving the flexibility of artificial evolution. By letting mutation affect not only controller genotypes, but also the mapping from genotype to phenotype, the future e effects of mutation can change over time. In this way, the need for prior parameter adaptation can be reduced. Experiments indicate that the ceGPM is capable of robustly adapting to a benchmark behavior. A comparison to a related approach shows significant improvements in evolvability.","PeriodicalId":370044,"journal":{"name":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","volume":"620 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evolvability in Evolutionary Robotics: Evolving the Genotype-Phenotype Mapping\",\"authors\":\"L. König, H. Schmeck\",\"doi\":\"10.1109/SASO.2010.27\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A completely evolvable genotype-phenotype mapping (ceGPM) is studied with respect to its capability of improving the flexibility of artificial evolution. By letting mutation affect not only controller genotypes, but also the mapping from genotype to phenotype, the future e effects of mutation can change over time. In this way, the need for prior parameter adaptation can be reduced. Experiments indicate that the ceGPM is capable of robustly adapting to a benchmark behavior. A comparison to a related approach shows significant improvements in evolvability.\",\"PeriodicalId\":370044,\"journal\":{\"name\":\"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems\",\"volume\":\"620 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2010-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/SASO.2010.27\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Fourth IEEE International Conference on Self-Adaptive and Self-Organizing Systems","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/SASO.2010.27","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Evolvability in Evolutionary Robotics: Evolving the Genotype-Phenotype Mapping
A completely evolvable genotype-phenotype mapping (ceGPM) is studied with respect to its capability of improving the flexibility of artificial evolution. By letting mutation affect not only controller genotypes, but also the mapping from genotype to phenotype, the future e effects of mutation can change over time. In this way, the need for prior parameter adaptation can be reduced. Experiments indicate that the ceGPM is capable of robustly adapting to a benchmark behavior. A comparison to a related approach shows significant improvements in evolvability.