Coarse-grain parallel genetic algorithms: categorization and new approach

This paper describes a number of different coarse-grain GA's, including various migration strategies and connectivity schemes to address the premature convergence problem. These approaches are evaluated on a graph partitioning problem. Our experiments showed, first, that the sequential GA's used are not as effective as parallel GA's for this graph partition problem. Second, for coarse-grain GA's, the results indicate that using a large number of nodes and exchanging individuals asynchronously among them is very effective. Third, GA's that exchange solutions based on population similarity instead of a fixed connection topology get better results without any degradation in speed. Finally, we propose a new coarse-grained GA architecture, the Injection Island GA (iiGA). The preliminary results of iiGA's show them to be a promising new approach to coarse-grain GA's.<<ETX>>

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

[2]  Reiko Tanese,et al.  Distributed Genetic Algorithms , 1989, ICGA.

[3]  Gunar E. Liepins,et al.  Some Guidelines for Genetic Algorithms with Penalty Functions , 1989, ICGA.

[4]  Richard J. Enbody,et al.  Further Research on Feature Selection and Classification Using Genetic Algorithms , 1993, ICGA.

[5]  David E. Goldberg,et al.  Finite Markov Chain Analysis of Genetic Algorithms , 1987, ICGA.

[6]  James E. Baker,et al.  Reducing Bias and Inefficienry in the Selection Algorithm , 1987, ICGA.

[7]  David R. Jefferson,et al.  Selection in Massively Parallel Genetic Algorithms , 1991, ICGA.

[8]  Kenneth A. De Jong,et al.  Genetic algorithms: A 10 Year Perspective , 1985, ICGA.

[9]  Kenneth Alan De Jong,et al.  An analysis of the behavior of a class of genetic adaptive systems. , 1975 .

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

[11]  Thomas Bäck,et al.  Genetic Algorithms and Evolution Strategies - Similarities and Differences , 1990, PPSN.

[12]  L. Darrell Whitley,et al.  The GENITOR Algorithm and Selection Pressure: Why Rank-Based Allocation of Reproductive Trials is Best , 1989, ICGA.

[13]  Michael J. Shaw,et al.  Genetic algorithms with dynamic niche sharing for multimodal function optimization , 1996, Proceedings of IEEE International Conference on Evolutionary Computation.

[14]  D. E. Goldberg,et al.  Genetic Algorithms in Search , 1989 .

[15]  L. Darrell Whitley,et al.  Optimization Using Distributed Genetic Algorithms , 1990, PPSN.

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

[17]  Heinz Mühlenbein,et al.  Parallel Genetic Algorithms, Population Genetics, and Combinatorial Optimization , 1989, Parallelism, Learning, Evolution.

[18]  David E. Goldberg,et al.  A Note on Boltzmann Tournament Selection for Genetic Algorithms and Population-Oriented Simulated Annealing , 1990, Complex Syst..

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

[20]  Bernard Manderick,et al.  Fine-Grained Parallel Genetic Algorithms , 1989, ICGA.

[21]  Gregor von Laszewski,et al.  Intelligent Structural Operators for the k-way Graph Partitioning Problem , 1991, ICGA.

[22]  Alice E. Smith,et al.  Genetic Optimization Using A Penalty Function , 1993, ICGA.

[23]  C. G. Shaefer,et al.  The ARGOT Strategy: Adaptive Representation Genetic Optimizer Technique , 1987, ICGA.

[24]  D. Ackley.  Stochastic iterated genetic hillclimbing , 1987 .