Clustering-Based Adaptive Crossover and Mutation Probabilities for Genetic Algorithms

Research into adjusting the probabilities of crossover and mutation pm in genetic algorithms (GAs) is one of the most significant and promising areas in evolutionary computation. px and pm greatly determine whether the algorithm will find a near-optimum solution or whether it will find a solution efficiently. Instead of using fixed values of px and pm , this paper presents the use of fuzzy logic to adaptively adjust the values of px and pm in GA. By applying the K-means algorithm, distribution of the population in the search space is clustered in each generation. A fuzzy system is used to adjust the values of px and pm. It is based on considering the relative size of the cluster containing the best chromosome and the one containing the worst chromosome. The proposed method has been applied to optimize a buck regulator that requires satisfying several static and dynamic operational requirements. The optimized circuit component values, the regulator's performance, and the convergence rate in the training are favorably compared with the GA using fixed values of px and pm. The effectiveness of the fuzzy-controlled crossover and mutation probabilities is also demonstrated by optimizing eight multidimensional mathematical functions

[1]  George E. P. Box,et al.  Evolutionary Operation: a Method for Increasing Industrial Productivity , 1957 .

[2]  K. Kit Sum.  Switch Mode Power Conversion: Basic Theory and Design , 1984 .

[3]  Kenneth A. De Jong,et al.  Are Genetic Algorithms Function Optimizers? , 1992, PPSN.

[4]  Shigeo Abe DrEng.  Pattern Classification , 2001, Springer London.

[5]  Jun Zhang,et al.  Implementation of a decoupled optimization technique for design of switching regulators using genetic algorithms , 2001 .

[6]  Henry Chung,et al.  Decoupled Optimization of Power Electronics Circuits Using Genetic Algorithms , 2000 .

[7]  K. Dejong,et al.  An analysis of the behavior of a class of genetic adaptive systems , 1975 .

[8]  Rajarshi Das,et al.  A Study of Control Parameters Affecting Online Performance of Genetic Algorithms for Function Optimization , 1989, ICGA.

[9]  Silvano Colombano,et al.  A Parallel Genetic Algorithm for Automated Electronic Circuit Design , 2000 .

[10]  David G. Stork,et al.  Pattern Classification (2nd ed.) , 1999 .

[11]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[12]  Kenneth A. De Jong,et al.  An Analysis of Multi-Point Crossover , 1990, FOGA.

[13]  John J. Grefenstette,et al.  Optimization of Control Parameters for Genetic Algorithms , 1986, IEEE Transactions on Systems, Man, and Cybernetics.

[14]  Kenneth A. De Jong,et al.  Genetic Algorithms are NOT Function Optimizers , 1992, FOGA.

[15]  Dongkyung Nam,et al.  Parameter optimization of a voltage reference circuit using EP , 1998, 1998 IEEE International Conference on Evolutionary Computation Proceedings. IEEE World Congress on Computational Intelligence (Cat. No.98TH8360).

[16]  M. Bialko,et al.  System for optimisation of electronic circuits using genetic algorithm , 1996, Proceedings of Third International Conference on Electronics, Circuits, and Systems.

[17]  Thomas Bäck,et al.  Evolutionary computation: comments on the history and current state , 1997, IEEE Trans. Evol. Comput..

[18]  Goldberg,et al.  Genetic algorithms , 1993, Robust Control Systems with Genetic Algorithms.

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

[20]  W. L. Lo,et al.  An Optimized Fuzzy Logic Controller for Active Power Factor Corrector Using Genetic Algorithm , 2000 .

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

[22]  J. Reed,et al.  Simulation of biological evolution and machine learning. I. Selection of self-reproducing numeric patterns by data processing machines, effects of hereditary control, mutation type and crossing. , 1967, Journal of theoretical biology.

[23]  George C. Verghese,et al.  Principles of power electronics , 1991 .

[24]  David B. Fogel,et al.  Evolutionary Computation: Towards a New Philosophy of Machine Intelligence , 1995 .

[25]  Ali M. S. Zalzala,et al.  Recent developments in evolutionary computation for manufacturing optimization: problems, solutions, and comparisons , 2000, IEEE Trans. Evol. Comput..

[26]  Ranga Vemuri,et al.  A genetic approach to simultaneous parameter space exploration and constraint transformation in analog synthesis , 1999, ISCAS'99. Proceedings of the 1999 IEEE International Symposium on Circuits and Systems VLSI (Cat. No.99CH36349).

[27]  Thomas Bäck,et al.  Evolutionary computation: Toward a new philosophy of machine intelligence , 1997, Complex..

[28]  Julius T. Tou,et al.  Pattern Recognition Principles , 1974 .