Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Designing efficient and accurate parallel genetic algorithms (parallel algorithms)
Combining mutation operators in evolutionary programming
IEEE Transactions on Evolutionary Computation
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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.