Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
The nature of niching: genetic algorithms and the evolution of optimal, cooperative populations
Introduction to Evolutionary Computing
Introduction to Evolutionary Computing
Fitness sharing and niching methods revisited
IEEE Transactions on Evolutionary Computation
On the role of population size and niche radius in fitness sharing
IEEE Transactions on Evolutionary Computation
A hybrid coevolutionary algorithm for designing fuzzy classifiers
Information Sciences: an International Journal
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In this paper, a novel niching approach to solve the multimodal function optimization problems is proposed. We firstly analyze and compare the characteristics and behaviors of a variety of niching methods as the fitness sharing, the crowding and deterministic crowding, the restricted mating, and the island model GA with regard to the competition, exploration & exploitation, genetic drift, and the ability to locate and maintain niches. Then we put forward the idea that the local competition of individuals is crucial to realize the distribution equilibria among niches of the optimization functions, and two types of niching methods, q-nearest neighbor replacement and parental neighbor replacement, are formulated by adopting special replacement policies in the setting of the SSGA. Finally, we use a set of test functions to illustrate the efficacy and efficiency of the proposed methods and the DC scheme based on the SSGA.