On Modelling Evolutionary Algorithm Implementations through Co-operating Populations
In this paper we present a framework for modelling Simple and Parallel Evolutionary Algorithm implementations as Co-operating Populations. Using this framework, a method called Co-operating Populations with Different Evolution Behaviours (CoPDEB), for generalizing and improving the performance of Parallel Evolutionary Algorithms (PEAs) is also presented. The main idea of CoPDEB is to maintain a number of populations exhibiting different evolution behaviours. CoPDEB was tested on three problems (the optimization of a real function, the TSP problem and the problem of training a Recurrent Artificial Neural Network), and appears to significantly increase the problemsolving capabilities over PEAs with the same evolution behaviour on each population. This paper also studies the effect of the migration rate (Epoch) and the population size on the performance of both PEAs and CoPDEB.
KeywordsSearch Space Evolution Behaviour Parallel Genetic Algorithm Recombination Probability Recombination Operator
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- 4.E. Cantú-Paz, A survey of parallel genetic algorithms. Calculateurs Paralleles, Reseaux et Systems Repartis 10, No. 2 (1998) pp. 141–171Google Scholar
- 6.D. E. Goldberg, H. Kargupta, J. Horn, and E. Cantu-Paz, “Critical Deme Size for Serial and Parallel Genetic Algorithms”, IlliGAL Report No. 95002, Illinois Genetic Algorithms Lab., Univ of Illinois at Urbana-Champaign, 1995Google Scholar
- 7.V. Petridis and S. Kazarlis, Varying quality function in genetic algorithms and the cutting problem, in “Proc First IEEE CEC”, vol I, pp. 166–169, IEEE, 1994Google Scholar
- 8.V. Petridis and A. Papaikonomou, Recurrent Neural Networks as Pattern Generators, in “Proc IEEE International Conference on Neural Networks”, pp. 872–875, 1994Google Scholar
- 9.M. Srinivas and L. M. Patnaik, Adaptive Probabilities of Crossover and Mutation in Genetic Algorithms. IEEE Transactions on Systems, Man and Cybernetics, 24 (4).Google Scholar