Dynamic Crossover and Mutation Genetic Algorithm Based on Expansion Sampling

Authors:
Min Dong;Yan Wu
Affiliations:
Department of Computer Science and Technology, Tongji University, Shanghai, China 200092;Department of Computer Science and Technology, Tongji University, Shanghai, China 200092
Venue:
AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
Year:
2009

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Abstract

The traditional genetic algorithm gets in local optimum easily, and its convergence rate is not satisfactory. So this paper proposed an improvement, using dynamic cross and mutation rate cooperate with expansion sampling to solve these two problems. The expansion sampling means the new individuals must compete with the old generation when create new generation, as a result, the excellent half ones are selected into the next generation. Whereafter several experiments were performed to compare the proposed method with some other improvements. The results are satisfactory. The experiment results show that the proposed method is better than other improvements at both precision and convergence rate.