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IEEE Transactions on Computers
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IEEE Transactions on Computers
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ICCAD '93 Proceedings of the 1993 IEEE/ACM international conference on Computer-aided design
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In this paper an efficient dynamic segregative genetic algorithm for optimizing variable order in Reduced Ordered Binary Decision Diagrams is presented. The approach integrates a basic genetic algorithm and uses a feature function in order to define a similarity measure between chromosomes. Subpopulations of individuals, formed by applying a clustering procedure in the feature space, are explored in parallel by multiple copies of the basic genetic algorithm. A communication protocol preserves the similarity inside each subpopulation during the evolution process. The redundant exploration of the search space is avoided by using a tabu search associative memory. Genetic material from yet unexplored regions of the search space is managed and organized in order to explicitly guide the search process to yet undiscovered local optima. The experimental evaluation of the algorithm uses classical benchmark problems, known to be very difficult. Experiments suggest that our approach has a better performance in terms of stability and quality of the solution, when compared to other heuristics, such as local search methods, basic genetic algorithms, a cellular genetic algorithm and even the static segregative genetic algorithm that was the starting point of this work. The quality of the distributed implementation and the communication protocol are thoroughly analyzed.