Abstract
Crossover is a main searching operator of genetic algorithms (GAs), which has distinguished GAs from many other algorithms. Through analyzing and imitating the implementation of crossover operator, this paper points out that crossover is intrinsically a heuristic mutation with reference. Its reference objective is just the other individual which is mated with the one which will be crossovered. On the basis of this conclusion this paper then explains and discusses the results obtained by other GA researchers through experiments.
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References
John H. Holland, Aadaptation in Natural and Artificial Systems, MIT Press, 1992, Second edition.
David E. Goldberg, Genetic Algorithms in search, Optimization, and Machine Learning, Addison-Wesley Publishing Company, 1989.
Melanie Mitchell, An Introduction to Genetic Algorithms, MIT Press, 1996.
Zbigniew Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer, 1994, Second Extended Edition.
Rechard K. Belew and Michael D. Vose ed., Foundations of Genetic Algorithms IV, Morgan Kaufamann Publishers, 1997.
Lance Chambers ed., Practical Handbook of Genetic Algorithms, Volume I-II, CRC, 1995.
Joachim Stender, Parallel Genetic Algorithms: Theory and Applications, IOS Press, 1993.
Thomas Bäck ed., Proceedings of the Seventh International Conference on Genetic Algorithms, Morgan Kaufamann Publishers, 1997.
Hans-Paul Schwefel, Evolution and Optimum seeking, John Wiley&Sons, 1994.
Thomas Bäck ed., Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms, Oxford University Press, 1996.
Elie Sanchez etc. Ed., Genetic Algorithms and Fuzzy Logic Systems: Soft Computing Perspectives, World Scientific Publishing, 1997.
K.F. Man etc., Genetic Algorithms for Control and Signal Processing (Advances in Industrial Control), Springer Verlag, 1997.
G. Winter etc. Ed., Genetic Algorithms in Engineering and Computer Science, John Wiley and Sons, 1996
T. Bäck, U. Hammel, H.-P. Schwefel, Evolutionary computation: comments on the history and current state, IEEE Tran. on Evolutionary Computation, 1997, 11, pp.3–17.
D.B. Fogel, J.W. Atmar, Comparing genetic operators with Gaussian mutations in simulated evolutionary processes using linear systems, Biological Cybernetics, 1990, 63, pp111–114.
J.D. Schaffer, L.J. Eshelman, On crossover as an evolutionarily viable strategy, Proceedings of the Fourth International Conference on Genetic Algorithms and Their Applications, Morgan Kaufmann, 1991.
William M. Spears, The Role of Mutation and Recombination in Evolutionary, Ph.D. Thesis, George Mason University, 1998
Mitchell A. Potter, The Design and Analysis of a Computational Model of Cooperative Coevolution, Ph.D. Thesis, George Mason University, 1997.
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© 2001 Springer-Verlag Berlin Heidelberg
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Tian, L. (2001). The Nature of Crossover Operator in Genetic Algorithms. In: Ziarko, W., Yao, Y. (eds) Rough Sets and Current Trends in Computing. RSCTC 2000. Lecture Notes in Computer Science(), vol 2005. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45554-X_78
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DOI: https://doi.org/10.1007/3-540-45554-X_78
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