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Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach

Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach

Ping-Teng Chang, Chih-Sheng Lin, Kuo-Chen Hung, Han-Hsiang Lee, Ching-Hsiang Chang
Copyright: © 2010 |Volume: 1 |Issue: 3 |Pages: 29
ISSN: 1947-3087|EISSN: 1947-3079|EISBN13: 9781609609535|DOI: 10.4018/jalr.2010070107
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MLA

Chang, Ping-Teng, et al. "Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach." IJALR vol.1, no.3 2010: pp.62-90. http://doi.org/10.4018/jalr.2010070107

APA

Chang, P., Lin, C., Hung, K., Lee, H., & Chang, C. (2010). Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach. International Journal of Artificial Life Research (IJALR), 1(3), 62-90. http://doi.org/10.4018/jalr.2010070107

Chicago

Chang, Ping-Teng, et al. "Collaboration and Competition Process: A Multi-Teams and Genetic Algorithm Hybrid Approach," International Journal of Artificial Life Research (IJALR) 1, no.3: 62-90. http://doi.org/10.4018/jalr.2010070107

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Abstract

The hybridization of genetic algorithms and the simplex method have been proven in literature as useful and promising in optimizations. Therefore, this paper proposes a multi-teams genetic-algorithm (MT-GA) hybrid developed toward extending the previous simplex-GA hybrids. The approach utilizes the simplex method as a united team and multi-teams collaboration and also competition search process in conjunction with the GAs. It is designed such that it has multi-teams with self-evolution (parallel applications of the simplex method), multi-teams communication and even mutual stimulation, and multi-teams survival competition as well as non-elite team breakup for individual relearning (with GAs) and re-forming the new teams. The extension of multi-teams GA thus provides the advantages and as previous simplex-GAs has been proved to outperform a number of other approaches. The experiments in this research show that the MT-GA generally outperforms the existing simplex-GAs for the indices of convergence rate (CPU time required), efficiency (number of function evaluations), and effectiveness (accuracy). Also, a further functional experiment of the MT-GA shows that the MT-GA can be a useful improved algorithm for the function optimization problems.

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