A New Diploid Scheme and Dominance Change Mechanism for Non-Stationary Function Optimization
Proceedings of the 6th International Conference on Genetic Algorithms
Supporting Polyploidy in Genetic Algorithms Using Dominance Vectors
EP '97 Proceedings of the 6th International Conference on Evolutionary Programming VI
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Forking genetic algorithms: Gas with search space division schemes
Evolutionary Computation
Adjusting population distance for the dual-population genetic algorithm
AI'07 Proceedings of the 20th Australian joint conference on Advances in artificial intelligence
Information Sciences: an International Journal
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In order to solve nonstationary optimization problems efficiently, evolutionary algorithms need sufficient diversity to adapt to environmental changes. The dual-population genetic algorithm (DPGA) is a novel evolutionary algorithm that uses an extra population called the reserve population to provide additional diversity to the main population through crossbreeding. Preliminary experimental results on various periods and degrees of environmental change have shown that the distance between the two populations of DPGA is one of the most important factors that affect its per-formance. However, it is very difficult to determine the best popu-lation distance without prior knowledge about the given problem. This paper proposes a new DPGA that uses two reserve populations (DPGA2). The reserve populations are at different distances from the main population. The information inflow from the reserve populations is controlled by survival selection. Experimental results show that DPGA2 shows a better performance than other evolutionary algorithms for nonstationary optimization problems without relying on prior knowledge about the problem.