A Novel Heuristic Genetic Algorithm Based on Simulated Annealing Strategy for Intelligent Computing

Hong Xiang Diao; Jian Xiao

doi:10.4028/www.scientific.net/AMR.143-144.619

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 143-144A Novel Heuristic Genetic Algorithm Based on...

A Novel Heuristic Genetic Algorithm Based on Simulated Annealing Strategy for Intelligent Computing

Abstract:

Intelligent computing is the application of advanced computing methods to improve performance in areas such as complex representations that are clear to users and easily modifiable. The goal is to make decision making more reliable, spontaneous and creative. In this paper, we put forwards a novel heuristic genetic algorithm based on simulated annealing strategy. Experimental results show the effectiveness of the proposed algorithm.

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Periodical:

Advanced Materials Research (Volumes 143-144)

Pages:

619-623

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.143-144.619

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Online since:

October 2010

Authors:

Hong Xiang Diao, Jian Xiao

Keywords:

Combinatorial Optimization, Genetic Algorithm (GA), Heuristic Algorithm , Intelligent Computing, Simulated Annealing Algorithm

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References

[1] P.A.N. Bosnan,D. Thierens, The balance between proximity and diversity in multi-objective evolutionary algorithm, IEEE Trans. Evolutionary Comput. 7(203)174-188.

DOI: 10.1109/tevc.2003.810761

Google Scholar

[2] K. Deb,A. Pratap,S. Agarwal,T. Meyarivan. A fast and elitist multi-objective genetic algorithm: NSGA-II, IEEE Trans. Evolutionary Comput. 6 (2002)182-197.

DOI: 10.1109/4235.996017

Google Scholar

[3] K.H. Han J.H. Moore, Genetic quantum algorithm and its application to combinatorial optimization problem[A]. Proceedings of the 2000 IEEE Congress on Evolutionary Computation[C], USA: IEEE Press, (2000).

DOI: 10.1109/cec.2000.870809

Google Scholar

[4] L.C. Jiao,L. Wang, A novel genetic algorithm based on immunity, IEEE Trans. on System, Man and Cybernatic, 30(2000)5, 552-561.

Google Scholar

[5] P. P. C. Yip and Y. H. Pao, Combinational optimization with use of guided evolutionary simulated annealing, _ IEEE Transactions on Neural Networks, vol. 6, no. 2, pp.290-295, Mar. (1995).

DOI: 10.1109/72.363466

Google Scholar