A New Self-adaptative Crossover Operator for Real-Coded Evolutionary Algorithms
In this paper we propose a new self-adaptative crossover operator for real coded evolutionary algorithms. This operator has the capacity to simulate other real-coded crossover operators dynamically and, therefore, it has the capacity to achieve exploration and exploitation dynamically during the evolutionary process according to the best individuals. In other words, the proposed crossover operator may handle the generational diversity of the population in such a way that it may either generate additional population diversity from the current one, allowing exploration to take effect, or use the diversity previously generated to exploit the better solutions.
In order to test the performance of this crossover, we have used a set of test functions and have made a comparative study of the proposed crossover against other classic crossover operators. The analysis of the results allows us to affirm that the proposed operator has a very suitable behavior; although, it should be noted that it offers a better behavior applied to complex search spaces than simple ones.
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- 1.Holland, J.H.: Adaptation in Natural and Artificial Systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 2.Goldberg, D.: Genetic Algorithm in Search, Optimisation, and Machine Learning. Addison-Wesley, Ann Abor (1989)Google Scholar
- 3.Lucasius, C.B., Kateman, G.: Applications of genetic algorithms in chemometrics. In: Proc. of the 3rd Int Conf on Genetic Algorithms, pp. 170–176. Morgan Kaufmann, San Francisco (1989)Google Scholar
- 7.Kawabe, T., Tagami, T.: A real coded genetic algorithm for matrix inequality design approach of robust PID controller with two degrees of freedom. In: Proc. of the 1997 IEEE Int. Symp. on Intelligent Control, pp. 119–124 (1997)Google Scholar
- 15.Michalewicz, Z.: Genetics Algorithms + Data Structures = Evolution Programs. WNT, Warsaw (1996)Google Scholar
- 18.Wright, A.H.: Genetic Algorithms for Real Parameter Optimization. Foundations of genetic algorithms (1991)Google Scholar
- 19.Syswerda, G.: Uniform crossover in genetic algorithms. In: Schaffer, J.D. (ed.) ICGA-89, Morgan Kaufmann, San Francisco (1989)Google Scholar
- 20.Eshelman, L.J., Caruana, R.A., Schaffer, J.D.: Biases in the Crossover Landscape (1997)Google Scholar
- 21.Agrawal, R.B.: Simulated binary crossover for real-coded genetic algorithms. Indian Institute of Technology, Kanpur (1995)Google Scholar
- 23.De Jong, K.E.: An analysis of the behavior of a class of genetic adaptive systems. University of Michigan Press, Ann Arbor (1975)Google Scholar
- 25.Torn, A., Zilinskas, A.: Global Optimization. Springer, Berlin (1989)Google Scholar