Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Proceedings of the third international conference on Genetic algorithms
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
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Predictive models for the breeder genetic algorithm i. continuous parameter optimization
Evolutionary Computation
Searching for diverse, cooperative populations with genetic algorithms
Evolutionary Computation
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Natural Computing: an international journal
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This paper considers a new method that enables a genetic algorithm (GA) to identify and maintain multiple optima of a multimodal function, by creating subpopulations within the niches defined by the multiple optima, thus warranting a good "diversity". The algorithm is based on a splitting of the traditional GA into a sequence of two processes. Since the GA behavior is determined by the exploration / exploitation balance, during the first step (Exploration), the multipopulation genetic algorithm coupled with a speciation method detects the potential niches by classifying "similar" individuals in the same population. Once the niches are detected, the algorithm achieves an intensification (Exploitation), by allocating a separate portion of the search space to each population. These two steps are alternately performed at a given frequency. Empirical results obtained with F6 Schaffer's function are then presented to show the reliability of the algorithm.