A diversity-control-oriented genetic algorithm (DCGA): performance in function optimization

In genetic algorithms, in order to attain the global optimum without getting stuck at a local optimum, an appropriate diversity of structures in the population needs to be maintained. I have proposed a new genetic algorithm called DCGA (diversity control-oriented genetic algorithm) to attain this goal. In DCGA, the structures of the population in the next generation are selected from the merged population of parents and their offspring on the basis of a particular selection probability to maintain the diversity of the structures. The major feature is that the distance between a structure and the best performance structure is used as the primary selection criterion and it is applied on the basis of a probabilistic function that produces a larger selection probability for a structure with a larger distance. The performance of DCGA in function optimization is examined by experiments on benchmark problems. Within the range of my experiments, DCGA showed superior performance and it seems to be a promising competitor of the previously proposed algorithm.

[1]  David E. Goldberg,et al.  Probabilistic Crowding: Deterministic Crowding with Probabilistic Replacement , 1999 .

[2]  R. Hinterding,et al.  Gaussian mutation and self-adaption for numeric genetic algorithms , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.

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

[4]  Thomas C. Peachey,et al.  The Nature of Mutation in Genetic Algorithms , 1995, ICGA.

[5]  Michael L. Mauldin,et al.  Maintaining Diversity in Genetic Search , 1984, AAAI.

[6]  Keith E. Mathias,et al.  The Effects of Control Parameters and Restarts on Search Stagnation in Evolutionary Programming , 1998, PPSN.

[7]  Xin Yao,et al.  Fast Evolution Strategies , 1997, Evolutionary Programming.

[8]  Hisashi Shimodaira,et al.  DCGA: a diversity control oriented genetic algorithm , 1997, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence.

[9]  H. Shimodaira.  DCGA: a diversity control oriented genetic algorithm , 1997 .

[10]  Thomas Bäck,et al.  An Overview of Evolutionary Algorithms for Parameter Optimization , 1993, Evolutionary Computation.

[11]  William E. Hart,et al.  Optimizing an Arbitrary Function is Hard for the Genetic Algorithm , 1991 .

[12]  L. Darrell Whitley,et al.  Building Better Test Functions , 1995, ICGA.

[13]  David E. Goldberg,et al.  Genetic Algorithms with Sharing for Multimodalfunction Optimization , 1987, ICGA.

[14]  Hisashi Shimodaira,et al.  A new genetic algorithm using large mutation rates and population-elitist selection (GALME) , 1996, Proceedings Eighth IEEE International Conference on Tools with Artificial Intelligence.

[15]  Larry J. Eshelman,et al.  The CHC Adaptive Search Algorithm: How to Have Safe Search When Engaging in Nontraditional Genetic Recombination , 1990, FOGA.

[16]  Samir W. Mahfoud.  Crowding and Preselection Revisited , 1992, PPSN.

[17]  L. Darrell Whitley,et al.  Transforming the search space with Gray coding , 1994, Proceedings of the First IEEE Conference on Evolutionary Computation. IEEE World Congress on Computational Intelligence.

[18]  L. Darrell Whitley,et al.  Test driving three 1995 genetic algorithms: New test functions and geometric matching , 1995, J. Heuristics.

[19]  J. David Schaffer,et al.  Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms , 1988, ML.

[20]  N. Mori,et al.  A thermodynamical selection rule for the genetic algorithm , 1995, Proceedings of 1995 IEEE International Conference on Evolutionary Computation.