Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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Combining Genetic Algorithms with Memory Based Reasoning
Proceedings of the 6th International Conference on Genetic Algorithms
Genetic algorithms with memory-and elitism-based immigrants in dynamic environments
Evolutionary Computation
Novel Associative Memory Retrieving Strategies for Evolutionary Algorithms in Dynamic Environments
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
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In this paper a method to increase the optimization ability of genetic algorithms (GAs) is proposed. To promote population diversity, a fraction of the worst individuals of the current population is replaced by individuals from an older population. To experimentally validate the approach we have used a set of well-known benchmark problems of tunable difficulty for GAs, including trap functions and NK landscapes. The obtained results show that the proposed method performs better than standard GAs without elitism for all the studied test problems and better than GAs with elitism for the majority of them.