Asymptotic guarantee of success for multi-agent memetic systems

Bulletin of the Polish Academy of Sciences. Technical Sciences

C. CottaDepartamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga ETSI Informática, Campus de Teatinos, 29071-Málaga, SpainOther articles by this author:De Gruyter OnlineGoogle Scholar¹  A. ByrskiCorresponding authorDepartment of Computer Science, AGH University of Science and Technology, 30 Mickiewicza Av., 30-059 Kraków, PolandEmailOther articles by this author:De Gruyter OnlineGoogle Scholar²  R. SchaeferDepartment of Computer Science, AGH University of Science and Technology, 30 Mickiewicza Av., 30-059 Kraków, PolandOther articles by this author:De Gruyter OnlineGoogle Scholar²  M. Smo?kaDepartment of Computer Science, AGH University of Science and Technology, 30 Mickiewicza Av., 30-059 Kraków, PolandOther articles by this author:De Gruyter OnlineGoogle Scholar² 

[1] Departamento de Lenguajes y Ciencias de la Computación, Universidad de Málaga ETSI Informática, Campus de Teatinos, 29071-Málaga, Spain;Department of Computer Science, AGH University of Science and Technology, 30 Mickiewicza Av., 30-059 Kraków, Poland

关键词: Keywords : computational multi-agent systems;    asymptotic analysis;    global optimization;    parallel evolutionary algorithms;    Markov chain modeling;   

DOI  :  10.2478/bpasts-2013-0025

学科分类：工程和技术（综合）

来源: Polska Akademia Nauk * Centrum Upowszechniania Nauki / Polish Academy of Sciences, Center for the Advancement of Science

【 摘 要 】

The paper introduces a stochastic model for a class of population-based global optimization meta-heuristics, that generalizes existing models in the following ways. First of all, an individual becomes an active software agent characterized by the constant genotype and the meme that may change during the optimization process. Second, the model embraces the asynchronous processing of agent’s actions. Third, we consider a vast variety of possible actions that include the conventional mixing operations (e.g. mutation, cloning, crossover) as well as migrations among demes and local optimization methods. Despite the fact that the model fits many popular algorithms and strategies (e.g. genetic algorithms with tournament selection) it is mainly devoted to study memetic algorithms.

【 授权许可】

Unknown