The Search on Mathematical Model of Evolutionary Computation

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Evolutionary algorithm is a random search method. In recent decades, various evolutionary algorithms emerge in endlessly by imitating population activity or the law of nature, so that evolutionary computation has been developed rapidly. The study of mathematical model about evolutionary algorithm is very lack. Only a small amount of research has been done. Through the study of the matrix model of evolutionary computation, this paper discusses the evolutionary process based on the matrix model in detail, and the working principle of matrix model and physical significance. The improvement suggestions of evolutionary algorithm have been given from the angle of the matrix model.

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1568-1572

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November 2014

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© 2014 Trans Tech Publications Ltd. All Rights Reserved

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[1] S. M. Bhandarkar, and H. Zhang, Image segmentation using evolutionary computation, Ieee Transactions on Evolutionary Computation, vol. 3, no. 1, pp.1-21, Apr, (1999).

DOI: 10.1109/4235.752917

Google Scholar

[2] M. C. Chen, and S. H. Huang, Credit scoring and rejected instances reassigning through evolutionary computation techniques, Expert Systems with Applications, vol. 24, no. 4, pp.433-441, May, (2003).

DOI: 10.1016/s0957-4174(02)00191-4

Google Scholar

[3] F. Herrera, M. Lozano, and J. L. Verdegay, Tackling real-coded genetic algorithms: Operators and tools for behavioural analysis, Artificial Intelligence Review, vol. 12, no. 4, pp.265-319, Aug, (1998).

DOI: 10.1023/a:1006504901164

Google Scholar

[4] D. Smith, Intelligence through simulated evolution: Forty years of evolutionary programming, Journal of the Operational Research Society, vol. 56, no. 3, pp.352-352, Mar, (2005).

Google Scholar

[5] W. G. S. Hines, EVOLUTIONARY STABLE STRATEGIES - A REVIEW OF BASIC THEORY, Theoretical Population Biology, vol. 31, no. 2, pp.195-272, Apr, (1987).

DOI: 10.1016/0040-5809(87)90029-3

Google Scholar

[6] P. G. Espejo, S. Ventura, and F. Herrera, A Survey on the Application of Genetic Programming to Classification, Ieee Transactions on Systems Man and Cybernetics Part C-Applications and Reviews, vol. 40, no. 2, pp.121-144, Mar, (2010).

DOI: 10.1109/tsmcc.2009.2033566

Google Scholar

[7] R. G. Reynolds, and B. Peng, Cultural Algorithms: Computational Modeling of How Cultures Learn to Solve Problems: An Engineering Example, Cybernetics & Systems, vol. 36, no. 8, pp.753-771, (2005).

DOI: 10.1080/01969720500306147

Google Scholar

[8] J. Xu, M. Zhang, and Y. Cai, Cultural Ant Algorithm for Continuous Optimization Problems, Applied Mathematics & Information Sciences, vol. 7, no. 2, pp.705-710, Jun, (2013).

DOI: 10.12785/amis/072l47

Google Scholar

[9] H. Ishibuchi, T. Yoshida, and T. Murata, Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling, Ieee Transactions on Evolutionary Computation, vol. 7, no. 2, pp.204-223, Apr, (2003).

DOI: 10.1109/tevc.2003.810752

Google Scholar

[10] H. Wang, I. Moon, S. Yang et al., A memetic particle swarm optimization algorithm for multimodal optimization problems, Information Sciences, vol. 197, pp.38-52, Aug 15, (2012).

DOI: 10.1016/j.ins.2012.02.016

Google Scholar