Stochastic Populations, Power Law and Fitness Aggregation in Genetic Algorithms
文献类型：期刊论文
| 作者 | Ma ZS[*]1,2 |
| 刊名 | FUNDAMENTA INFORMATICAE
 |
| 出版日期 | 2013 |
| 卷号 | 122期号:3页码:173-206 |
| 关键词 | Genetic Algorithm (GA) Spatial Distribution Pattern Power Law Dynamic Population Fitness Distribution Fitness Aggregation Fitness Aggregation Dynamics Insect Population Dynamics |
| 通讯作者 | ma@vandals.uidaho.edu |
| 合作状况 | 其它 |
| 英文摘要 | Natural populations are dynamic in both time and space. In biological populations such as insects, spatial distribution patterns are often studied as the first step to characterize population dynamics. In nature, the spatial distribution patterns of insect populations are considered as the emergent expression (property) of individual behaviors at population levels and are fine-tuned or optimized by natural selection. This inspiration prompts us to investigate the possibly similar mechanisms in Genetic Algorithms (GA) populations. In this study, we introduce the mathematical models for the spatial distribution patterns of insect populations to GA with the conjecture that the emulation of biological populations in nature may lead to computational improvement. In particular, we introduce three modeling approaches from the research of spatial distribution patterns of insect populations: (i) probability distribution modeling approach, (ii) aggregation index approach, and (iii) Taylor's (1961, 1977) Power Law, Iwao's (1968, 1976) Mean Crowding Model and Ma's (1991c) population aggregation critical density (PACD), to characterize populations in GA. With these three approaches, we investigate four mappings from the research field of insect spatial distribution patterns to GA populations in order to search for possible counterpart mechanisms or features in GA. They are: (i) mapping insect spatial distribution patterns to GA populations or allowing GA populations to be controlled by stochastic distribution models that describe insect spatial distributions; (ii) mapping insect population distribution to GA population fitness distribution via Power Law and PACD modeling; (iii) mapping population aggregation dynamics to GA fitness progression across generations (or fitness aggregation dynamics in GA) via insect population aggregation index; (iv) mapping insect population sampling model to optimal GA population sizing. With regard to the mapping (i), the experiment results show the significant improvements in GA computational efficiency in terms of the reduced fitness evaluations and associated costs. This prompts us to suggest using probability distribution models, or what we call stochastic GA populations, to replace the fixed-size population settings. We also found the counterpart for the second mapping, the wide applicability of Power Law and Mean Crowding model to the fitness distribution in GA populations. The testing of the third and fourth mappings is very preliminary; we use example cases to suggest two further research problems: the potential to use fitness aggregation dynamics for controlling the number of generations iterated in GA searches, and the possibility to use fitness aggregation distribution parameters [(obtained in mapping (ii)] in determining the optimum population size in GA. A third interesting research problem is to investigate the relationship between mapping (i) and (iii), i. e., the controlling of both population sizes and population generations. |
| 收录类别 | SCI |
| 资助信息 | This research has received funding from the following funding sources: Na- tional Natural Science Foundation of China (Grant No: 61175071, “Ecological ‘Theater’and Evolution- ary Computing ‘Play”’), “The One Hundred PIs of the Exceptional Talents in Science and Technology” of the Chinese Academy of Sciences, and the startup fund from the State Key Laboratory of Genetic Resources and Evolution of China, and “Top-Talents Project” from Yunnan Province, “The Top Tal- ents (from Overseas) Program of Yunnan Province” and “Innovative Research Initiative of the Synergy between Natural Evolution and Computational Evolution” of CAS-Yunan Province. |
| 语种 | 英语 |
| WOS记录号 | WOS:000317267100001 |
| 公开日期 | 2013-05-09 |
| 源URL | [http://159.226.149.42:8088/handle/152453/7417]  |
| 专题 | 昆明动物研究所_计算生物与生物信息学 昆明动物研究所_遗传资源与进化国家重点实验室 |
| 作者单位 | 1.Computational Biology and Medical Ecology Lab State Key Laboratory of Genetic Resources and Evolution, Kunming Institute of Zoology Chinese Academy of Sciences, China 2.Computer Science Department, University of Idaho, Moscow, ID 83844, USA |
| 推荐引用方式 GB/T 7714 | Ma ZS[*]. Stochastic Populations, Power Law and Fitness Aggregation in Genetic Algorithms[J]. FUNDAMENTA INFORMATICAE,2013,122(3):173-206. |
| APA | Ma ZS[*].(2013).Stochastic Populations, Power Law and Fitness Aggregation in Genetic Algorithms.FUNDAMENTA INFORMATICAE,122(3),173-206. |
| MLA | Ma ZS[*]."Stochastic Populations, Power Law and Fitness Aggregation in Genetic Algorithms".FUNDAMENTA INFORMATICAE 122.3(2013):173-206. |
入库方式： OAI收割
来源：昆明动物研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。