Top > JACIII > Advanced Genetic Algorithms Based on Adaptive Partitioning Met ...

single-jc.php

JACIII Vol.11 No.6 pp. 677-680
doi: 10.20965/jaciii.2007.p0677
(2007)

Paper:

Views over last 60 days: 386

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

Received:
February 25, 2007
Accepted:
March 20, 2007
Published:
July 20, 2007
Creative Commons License
Keywords:
genetic algorithms, adaptive partitioning method
Abstract
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.
Cite this article as:
C. Han and H. Nobuhara, “Advanced Genetic Algorithms Based on Adaptive Partitioning Method,” J. Adv. Comput. Intell. Intell. Inform., Vol.11 No.6, pp. 677-680, 2007.
Data files:
References
  1. [1] J. H. Holland, “Adaptation in natural and artificial systems,” Ann Arbor, MI, University of Michigan, 1975.
  2. [2] D. E. Goldberg, “Genetic algorithms in search, optimization and machine learning,” Addison-Wesley, Reading, MA, 1989.
  3. [3] A. H. Mantawy, Y. L. Abdel-Magid, and S. Z. Selim, “Integrating genetic algorithms, tabu search, and simulated annealing for the unit commitment problem,” IEEE Trans. Power Systems, Vol.14, No.3, pp. 829-836, Aug. 1999.
  4. [4] B. Li and W. Jiang, “A novel stochastic optimization algorithm,” IEEE Trans. Systems, Man, and Cybernetics-Part B, Vol.30, No.1, pp. 193-198, Feb. 2000.
  5. [5] A. M. Sabatini, “A hybrid genetic algorithm for estimating the optimal time scale of linear systems approximations using Laguerre models,” IEEE Trans. Automatic Control, Vol.45, No.5, pp. 1007-1011, May 2000.
  6. [6] G. Alpaydin, G. Dundar, and S. Balkir, “Evolution-based design of neural fuzzy networks using self-adapting genetic parameters,” IEEE Trans. Fuzzy Systems, Vol.10, No.2, pp. 211-221, Apr. 2002.
  7. [7] Z. B. Tang, “Partitioned Random Search to Optimization,” Proc. of the American Control Conference, San Francisco, 1993.
  8. [8] K. De. Jong, “An Analysis of the Behavior of a Class of Genetic Adaptive Systems,” Ph.D. dissertation, Dept. Computer Sci., Univ. Michigan, Ann Arbor, MI, 1975.
  9. [9] M. Dorigo and L. M. Gambardella, “Ant colony system: A cooperative learning approach to the traveling salesman problem,” IEEE Trans. Evolutionary Computation, Vol.1, No.1, pp. 53-66, 1997.

Creative Commons License  This article is published under a Creative Commons Attribution-NoDerivatives 4.0 Internationa License.

*This site is desgined based on HTML5 and CSS3 for modern browsers, e.g. Chrome, Firefox, Safari, Edge, Opera.

Last updated on Oct. 11, 2024