Exploration/exploitation tradeoff with cell-shift and heuristic crossover for evolutionary algorithms

Journal of Systems Science and Complexity

Volumn 20, Issue 1, 2007, Pages 66-74

  Zhao, Xinchao ^a   Hao, Junling ^b  

^a BEIJING UNIVERSITY OF POSTS AND TELECOMMUNICATIONS   (China)

^b UNIVERSITY OF INTERNATIONAL BUSINESS AND ECONOMICS   (China)

Author keywords

Cell shift crossover; Evolutionary algorithm; Exploration exploitation tradeoff; Heuristic crossover; Numerical optimization

Indexed keywords

EID: 33847310351     PISSN: 10096124     EISSN: 15597067     Source Type: Journal    
DOI: 10.1007/s11424-007-9005-6     Document Type: Article

References (20)

1. D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Reading, Addison-Wesley, MA, 1989
2. T. Back, D. Fogel, and Z. Michalewicz, Handbook of Evolutionary Computation, Oxford Univ. Press, London, 1997.
3. E. Alba and B. Dorronsoro, The exploration/exploitation tradeoff in dynamic cellular genetic algorithms, IEEE Trans. Evol. Comput, 2005, 9(2): 126-142.
4. T. Kuo and S. Y. Huwang, A genetic algorithm with disruptive selection, IEEE Trans. Syst., Man and Cybern., 1996, 126(2): 299-307.
5. J. C. Potts, T. D. Giddens, and S. B. Yadav, The development and evaluation of an improved genetic algorithm based on migration and artificial selection, IEEE Trans. Syst., Man and Cybern., 1994, 124(1): 73-85.
6. L. Davis, Adapting operator probabilities in genetic algorithms, in Proc. 3rd Int. Conf. Genetic Algorithms (ed. by J. Schaffer), Morgan Kaufmann Publishers Inc., 1989, 61-69.
7. X. C. Zhao, A greedy genetic algorithm for unconstrained global optimization, Journal of Systems Science and Complexity, 2005, 18(1): 102-110.
8. X. C. Zhao and H. L. Long, Multiple bit encoding-based search algorithms, in Proc. 2005 IEEE Congress on Evolutionary Computation, IEEE Press, 2005, 1996-2001.
9. Y. Jin, A comprehensive survey of fitness approximation in evolutionary computation, Soft Computing, 2005, 9(1): 3-12.
10. F. Herrera and M. Lozano, Gradual Distributed Real-Coded Genetic Algorithms, IEEE Trans. Evol. Comput., 2000, 4(1): 43-63
11. B. Manderick and P. Spiessens, Fine-grained parallel genetic algorithm, in Proc. 3rd Int. Conf. Genetic Algorithms (ed. by J. Schaffer), Morgan Kaufmann Publishers Inc., 1989, 428-433.
12. H. Mühlenbein, M. Schomish, and J. Born, The parallel genetic algorithm as a function optimizer, Parallel Comput, 1991, 17: 619-632.
13. S. Baluja, Structure and performance of fine-grain parallelism in genetic search, in Proc. 5th Int. Conf. Genetic Algorithms (ed. by S. Forrest), Morgan Kaufmann Publishers Inc., 1993, 155-162.
14. D. Whitley, Cellular genetic algorithms, in Proc. 5th Int. Conf. Genetic Algorithms (ed. by S. Forrest), 1993, 658.
15. X. Yao, Y. Liu, and G. M. Lin, Evolutionary programming made faster, IEEE Trans. Evol. Comput, 1999, 3(2): 82-102.
16. J. He, J.Y. Xu, and X. Yao, Solving equations by Hybrid evolutionary computation techniques, IEEE Transactions on Evolutionary Computation, 2000, 4(3): 295-304.
17. Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, (3rd Edition), Springer, 1996.
18. D. H. Wolpert and W. G. Macready, No free lunch theorems for optimization, IEEE Trans. Evol. Comput, 1997, 1(1): 67-82.
19. E. Cantu-Paz, Efficient and Accurate Parallel Genetic Algorithms (2nd ed), Book Series on Genetic Algorithms and Evolutionary Computation, Vol. I, Norwell, MA: Kluwer, 2000.
20. S. Tsutsui and Y. Fujimoto, Forking genetic algorithm with blocking and shrinking modes, in Proc. 5th Int. Conf. Genetic Alorithms (ed. by S. Forrest), Morgan Kaufmann Publishers Inc., 1993, 206-213.