Genetic algorithms with sharing for multimodal function optimization
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
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Advances in genetic programming
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Parallel Computing
Sizing Populations for Serial and Parallel Genetic Algorithms
Proceedings of the 3rd International Conference on Genetic Algorithms
Efficiency of Local Genetic Algorithm in Parallel Processing
PDCAT '05 Proceedings of the Sixth International Conference on Parallel and Distributed Computing Applications and Technologies
Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence (Studies in Computational Intelligence)
Applied Computational Intelligence and Soft Computing - Special issue on theory and applications of evolutionary computation
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GEP is a biologically motivated machine learning technique used to solve complex multitude problems. Similar to other evolution algorithms, GEP is slow when dealing with a large number of population. Considering that the parallel GEP has great efficiency and the niching method can keep diversity in the process of exploring evolution, a niching GEP algorithm based on parallel model is presented and discussed in this paper. In this algorithm, dividing the population to the niche nodes in sub-populations can solves the same problem in less computation time than it would take on a single process. Experimental results on sequence induction, function finding and sunspot prediction demonstrate its advantages and show that the proposed method takes less computation time but with higher accuracy.