Chaotic populations in genetic algorithms

	Chaotic populations in genetic algorithms
	Ma ZS*; ma@vandals.uidaho.edu
	2012
发表期刊	APPLIED SOFT COMPUTING
卷号	12 期号:8 页码:2409-2424
合作性质	其它
摘要	We set two objectives for this study: one is to emulate chaotic natural populations in GA (Genetic Algorithms) populations by utilizing the Logistic Chaos map model, and the other is to analyze the population fitness distribution by utilizing insect spatial distribution theory. Natural populations are so dynamic that one of the first experimental evidences of Chaos in nature was discovered by a theoretical ecologist, May (1976, Nature, 261,459-467)[30], in his analysis of insect population dynamics. In evolutionary computing, perhaps influenced by the stable or infinite population concepts in population genetics, the status quo of population settings has dominantly been the fixed-size populations. In this paper, we propose to introduce dynamic populations controlled by the Logistic Chaos map model to Genetic Algorithms (GA), and test the hypothesis - whether or not the dynamic populations that emulate chaotic populations in nature will have an advantage over traditional fixed-size populations. The Logistic Chaos map model, arguably the simplest nonlinear dynamics model, has surprisingly rich dynamic behaviors, ranging from exponential, sigmoid growth, periodic oscillations, and aperiodic oscillations, to complete Chaos. What is even more favorable is that, unlike many other population dynamics models, this model can be expressed as a single parameter recursion equation, which makes it very convenient to control the dynamic behaviors and therefore easy to apply to evolutionary computing. The experiments show result values in terms of the fitness evaluations and memory storage requirements. We further conjecture that Chaos may be helpful in breaking neutral space in the fitness landscape, similar to the argument in ecology that Chaos may help the exploration and/or exploitation of environment heterogeneity and therefore enhance a species' survival or fitness. (C) 2012 Elsevier B. V. All rights reserved.
关键词	Genetic Algorithm Chaos Theory Logistic Chaos Map Dynamic Populations Fitness Aggregation Power Law
资助者	This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.” ; This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.” ; This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.” ; This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.”
收录类别	SCI
语种	英语
资助者	This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.” ; This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.” ; This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.” ; This research has received funding from the following fund- ing sources: National Science Foundation of China (Grant No: 61175071), “The One Hundred-PI (Principal Investigator) Plan for the Exceptional Talents in Science & Technology” of the Chinese Academy of Sciences, the startup fund from the State Key Labo- ratory of Genetic Resources and Evolution of China, and “Funding for the Top Talents Program in Science and Technology of Yunnan Province.”
WOS记录号	WOS:000305275800044
引用统计
文献类型	期刊论文
条目标识符	http://ir.kiz.ac.cn/handle/152453/7047
专题	科研部门_计算生物与医学生态学（马占山）遗传资源与进化国家重点实验室
通讯作者	ma@vandals.uidaho.edu
作者单位	Computational Biology and Medical Ecology Lab, State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, 650223, PR China
推荐引用方式 GB/T 7714	Ma ZS*,ma@vandals.uidaho.edu. Chaotic populations in genetic algorithms[J]. APPLIED SOFT COMPUTING,2012,12(8):2409-2424.
APA	Ma ZS*,&ma@vandals.uidaho.edu.(2012).Chaotic populations in genetic algorithms.APPLIED SOFT COMPUTING,12(8),2409-2424.
MLA	Ma ZS*,et al."Chaotic populations in genetic algorithms".APPLIED SOFT COMPUTING 12.8(2012):2409-2424.

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