Multi-Space Competitive DGA for Model Selection and its Application to Localization of Multiple Signal Sources

Autor:	Shudai Ishikawa, Ryosuke Kubota, Keiichi Horio, Hideaki Misawa, Takeshi Yamakawa, Tatsuji Tokiwa
Rok vydání:	2011
Předmět:	Human-Computer Interaction Artificial Intelligence business.industry Computer science Model selection Pattern recognition Computer Vision and Pattern Recognition Artificial intelligence Space (commercial competition) business Signal
Zdroj:	Journal of Advanced Computational Intelligence and Intelligent Informatics. 15:1320-1328
ISSN:	1883-8014 1343-0130
DOI:	10.20965/jaciii.2011.p1320
Popis:	In this paper, a new optimization method, which is effective for the problems that the optimum solution should be searched in several solution spaces, is proposed. The proposed method is an extension of Distributed Genetic Algorithm (DGA), in which each subpopulation searches a solution in the corresponding solution space. Through the competition between the sub-populations, population sizes are adequately and gradually changed. By the change of the population size, the appropriate sub-population attracts many individuals. The changing population size yield the efficient search for the problems of searching for solutions in multiple spaces. In order to evaluate the proposed method, it is applied to a polynomial curve fitting and signal source localization, in which the number of sources is preliminarily unknown. Simulation results show the effectiveness of the proposed method.
Databáze:	OpenAIRE
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