Abstract
In this article, a new interconnection model is proposed for Parallel Genetic Algorithm based crowding scheme. The crowding scheme is employed to maintain stable subpopulations at niches of a multi modal nonlinear function. The computational burden is greatly reduced by parallelizing the scheme based on the notion of coarse grained parallelization. The proposed interconnection model with a new crossover operator known as Generalized Crossover (GC) was found to maintain stable subpopulation for different classes and its performance was superior to that of the with two point crossover operators. Convergence properties of the algorithm is established and simulation results are presented to demonstrate the efficacy of the scheme.
This work is supported by the MHRD project on Biomedical Image Processing Using Genetic Algorithm
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© 2002 Springer-Verlag Berlin Heidelberg
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Nanda, P.K., Muni, D.P., Kanungo, P. (2002). Parallelized Crowding Scheme Using a New Interconnection Model. In: Pal, N.R., Sugeno, M. (eds) Advances in Soft Computing — AFSS 2002. AFSS 2002. Lecture Notes in Computer Science(), vol 2275. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45631-7_59
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DOI: https://doi.org/10.1007/3-540-45631-7_59
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