{"title":"形态学与控制协同进化的过早收敛性研究","authors":"Luis Eguiarte-Morett, Wendy Aguilar","doi":"10.1177/10597123231198497","DOIUrl":null,"url":null,"abstract":"This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.","PeriodicalId":55552,"journal":{"name":"Adaptive Behavior","volume":" ","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Premature convergence in morphology and control co-evolution: a study\",\"authors\":\"Luis Eguiarte-Morett, Wendy Aguilar\",\"doi\":\"10.1177/10597123231198497\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.\",\"PeriodicalId\":55552,\"journal\":{\"name\":\"Adaptive Behavior\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":1.2000,\"publicationDate\":\"2023-09-07\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Adaptive Behavior\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1177/10597123231198497\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Adaptive Behavior","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1177/10597123231198497","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Premature convergence in morphology and control co-evolution: a study
This article addresses the co-evolution of morphology and control in evolutionary robotics, focusing on the challenge of premature convergence and limited morphological diversity. We conduct a comparative analysis of state-of-the-art algorithms, focusing on QD (Quality-Diversity) algorithms, based on a well-defined methodology for benchmarking evolutionary algorithms. We introduce carefully chosen indicators to evaluate their performance in three core aspects: task performance, phenotype diversity, and genotype diversity. Our findings highlight MNSLC (Multi-BC NSLC), with the introduction of aligned novelty to NSLC (Novelty Search with Local Competition), as the most effective algorithm for diversity preservation (genotype and phenotype diversity), while maintaining a competitive level of exploitability (task performance). MAP-Elites, although exhibiting a well-balanced trade-off between exploitation and exploration, fall short in protecting morphological diversity. NSLC, while showing similar performance to MNSLC in terms of exploration, is the least performant in terms of exploitation, contrasting with QN (Fitness-Novelty MOEA), which exhibits much superior exploitation, but inferior exploration, highlighting the effects of local competition in skewing the balance toward exploration. Our study provides valuable insights into the advantages, disadvantages, and trade-offs of different algorithms in co-evolving morphology and control.
期刊介绍:
_Adaptive Behavior_ publishes articles on adaptive behaviour in living organisms and autonomous artificial systems. The official journal of the _International Society of Adaptive Behavior_, _Adaptive Behavior_, addresses topics such as perception and motor control, embodied cognition, learning and evolution, neural mechanisms, artificial intelligence, behavioral sequences, motivation and emotion, characterization of environments, decision making, collective and social behavior, navigation, foraging, communication and signalling.
Print ISSN: 1059-7123