Abstract
In this paper, we propose a self Adaptive Migration Model for Genetic Algorithms, where parameters of population size, the number of points of crossover and mutation rate for each population are fixed adaptively. Further, the migration of individuals between populations is decided dynamically. This paper gives a mathematical schema analysis of the method stating and showing that the algorithm exploits previously discovered knowledge for a more focused and concentrated search of heuristically high yielding regions while simultaneously performing a highly explorative search on the other regions of the search space. The effective performance of the algorithm is then shown using standard testbed functions, when compared with Island model GA(IGA) and Simple GA(SGA).
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© 2005 Springer-Verlag Berlin Heidelberg
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Srinivasa, K.G., Sridharan, K., Shenoy, P.D., Venugopal, K.R., Patnaik, L.M. (2005). A Dynamic Migration Model for Self-adaptive Genetic Algorithms. In: Gallagher, M., Hogan, J.P., Maire, F. (eds) Intelligent Data Engineering and Automated Learning - IDEAL 2005. IDEAL 2005. Lecture Notes in Computer Science, vol 3578. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11508069_72
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DOI: https://doi.org/10.1007/11508069_72
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-26972-4
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