Advanced Genetic Algorithms Based on Adaptive Partitioning Method
Chang-Wook Han* and Hajime Nobuhara**
*School of Electrical Engineering and Computer Science, Yeungnam University, 214-1 Dae-dong, Gyongsan, Gyongbuk, 712-749, South Korea
**Department of Intelligent Interaction Technologies, Graduate School of Systems and Information Engineering, University of Tsukuba, 1-1-1 Tennoudai, Tsukuba science city, Ibaraki 305-8573, Japan
Genetic algorithms (GA) are well known and very popular stochastic optimization algorithm. Although, GA is very powerful method to find the global optimum, it has some drawbacks, for example, premature convergence to local optima, slow convergence speed to global optimum. To enhance the performance of the GA, this paper proposes an adaptive genetic algorithm based on partitioning method. The partitioning method, which enables a genetic algorithm to find a solution very effectively, adaptively divides the search space into promising sub-spaces to reduce the complexity of optimization. This partitioning method is more effective as the complexity of the search space is increasing. The validity of the proposed method is confirmed by applying it to several bench mark test function examples and a traveling salesman problem.
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