Adaptive Genetic Algorithms Based on Fuzzy Techniques

The genetic algorithm behaviour is determined by the exploitation and exploration relationship kept throughout the run. Adap-tive genetic algorithms have been built for inducing suitable exploitation/exploration relationships for avoiding the premature convergence problem. Some adaptive genetic algorithms are built using fuzzy logic techniques. In this paper, we summarize two types of such approaches. The rst one concerns dynamic crossover operators based on parameterized fuzzy connectives and the second one deals with adaptive real-coded genetic algorithms based on the use of fuzzy logic controllers.

[1]  Francisco Herrera,et al.  Dynamic and heuristic fuzzy connectives-based crossover operators for controlling the diversity and convergence of real-coded genetic algorithms , 1996, Int. J. Intell. Syst..

[2]  Zbigniew Michalewicz,et al.  GAVaPS-a genetic algorithm with varying population size , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[3]  Hideyuki Takagi,et al.  Dynamic Control of Genetic Algorithms Using Fuzzy Logic Techniques , 1993, ICGA.

[4]  J. David Schaffer,et al.  An Adaptive Crossover Distribution Mechanism for Genetic Algorithms , 1987, ICGA.

[5]  Jacques Periaux,et al.  Genetic Algorithms in Engineering and Computer Science , 1996 .

[6]  Lawrence Davis,et al.  Adapting Operator Probabilities in Genetic Algorithms , 1989, ICGA.

[7]  José L. Verdegay,et al.  The use of fuzzy connectives to design real-coded genetic algorithms , 1994 .

[8]  M. Mizumoto.  Pictorial representations of fuzzy connectives, part I: cases of t-norms, t-conorms and averaging operators , 1989 .

[9]  James E. Baker,et al.  Adaptive Selection Methods for Genetic Algorithms , 1985, International Conference on Genetic Algorithms.

[10]  Lalit M. Patnaik,et al.  Adaptive probabilities of crossover and mutation in genetic algorithms , 1994, IEEE Trans. Syst. Man Cybern..

[11]  Thomas Bck,et al.  Self-adaptation in genetic algorithms , 1991 .

[12]  William M. Spears,et al.  Adapting Crossover in a Genetic Algorithm , 2007 .

[13]  Zbigniew Michalewicz,et al.  Genetic Algorithms + Data Structures = Evolution Programs , 1996, Springer Berlin Heidelberg.

[14]  Francisco Herrera,et al.  Fuzzy connectives based crossover operators to model genetic algorithms population diversity , 1997, Fuzzy Sets Syst..