Abstract
In this paper, a new model of Probabilistic Model-Building Genetic Algorithms (PMBGAs), Distributed PMBGA (DPMBGA), is proposed. In the DPMBGA, the correlation among the design variables is considered by Principal Component Analysis (PCA) when the off-springs are generated. The island model is also applied in the DPMBGA for maintaining the population diversity. Through the standard test functions, some models of DPMBGA are examined. The DPMBGA where PCA is executed in the half of the islands can find the good solutions in the problems whether or not the problems have the correlation among the design variables. At the same time, the search capability and some characteristics of the DPMBGA are also discussed.
Keywords
- Principle Component Analysis
- Genetic Algorithm
- Design Variable
- Search Capability
- Parallel Genetic Algorithm
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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© 2003 Springer-Verlag Berlin Heidelberg
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Hiroyasu, T., Miki, M., Sano, M., Shimosaka, H., Tsutsui, S., Dongarra, J. (2003). Distributed Probabilistic Model-Building Genetic Algorithm. In: Cantú-Paz, E., et al. Genetic and Evolutionary Computation — GECCO 2003. GECCO 2003. Lecture Notes in Computer Science, vol 2723. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45105-6_112
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DOI: https://doi.org/10.1007/3-540-45105-6_112
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