An adaptive evolutionary algorithm for numerical optimization
In this paper, a normalized floating point representation has been used for making it be possible to design biotechnical genetic operators as well as to apply some genetic operators like inversion. To improve the adaptation of evolutionary algorithms and avoid the biases which may exist in some genetic operators, we have designed and applied several kinds of genetic operators with some probability. The experimental results show that our adaptive evolutionary algorithm has a better performance than the BGA (Breeder genetic Algorithm) and GAFOC (Genetic Algorithm For Optimal Control problems) for the test problems.
Keywordssimulated evolution evolutionary algorithm genetic algorithm numerical optimization
Unable to display preview. Download preview PDF.
- 1.D. E. Goldberg, Genetic Algorithms in Search, Optimization and Machine Learning, Addison Wesley, 1989.Google Scholar
- 2.R.Hinterding, H.Gielewski and T.C.Peachey, The Nature of Mutation in Genetic Algorithms, In: Proc. of the Sixth Int'l Conf. on Genetic Algorithms, Morgan Kaufmann Publishers, San Francisco, 65–72, 1995.Google Scholar
- 3.Z. Michalewicz, Genetic Algorithms + Data Structures = Evolution Programs, Springer-Verlag, Berlin, 1992.Google Scholar
- 4.Z. Michalewicz, C.Z.Janikow and J.B.Krawczyk, A Modified Genetic Algorithms for Optimal Control Problems, Computers Math. Applic., 23(12), 83–94, 1992.Google Scholar
- 5.H.Mühlenbeln,M.Schomisch,J.Born, The Parallel Genetic Algorithm as Function Optimizer, Parallel Computing, 17, 619–632,1991.Google Scholar
- 6.H.Mühlenbein and D.Schlierkamp-Vose, Predictive Models for the Breeder Genetic Algorithm, Evolutionary Computation, 1(1), 25–49, 1993.Google Scholar
- 7.A.Törn and A.Zilinskas, Global Optimization, Lecture Notes in Computer Sciences, No.350, Springer-Verlag, Berlin, 1989.Google Scholar