Parallel evolutionary optimisation with constraint propagation
This paper describes a parallel model for a distributed memory architecture of a non traditional evolutionary computation method, which integrates constraint propagation and evolution programs. This integration provides a problem-independent optimisation strategy for large scale constrained combinatorial problems over finite integer domains. We have adopted a global parallelisation approach which preserves the properties, behaviour, and theoretical studies of the sequential algorithm. Moreover, high speedup is achieved since genetic operators are coarsegrained, as they perform a search in a discrete space carrying out constraint propagation. A global parallelisation implies a single population but, as we focus on distributed memory architectures, the single virtual population is physically distributed among the processors. Selection and mating consider all the individuals in the population, but the application of genetic operators is performed in parallel. The implementation of the model has been tested on a CRAY T3E multiprocessor using two complex constrained optimisation problems. Experiments have proved the efficiency of this approach since linear speedups have been obtained.
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- 1.CantÚ-Paz, E. A survey of parallel genetic algorithms. IlliGAL Report No. 97003 (1997).Google Scholar
- 2.Grefenstette, J.J. Parallel adaptive algorithms for function optimisation. Tech. Rep. No. CS-81-19. Nashville, TN: Vanderbilt University Computer Science Department. (1981)Google Scholar
- 3.Michalewicz, Z.: Genetic algorithms + Data Structures = Evolution Programs. 2nd Edition, Springer-Verlag (1994).Google Scholar
- 5.Paredis, J.: Genetic State-Search for constrained Optimisation Problems. 13th Int. Joint Conf. on Artificial Intelligence (1993).Google Scholar
- 6.Ruiz-Andino A., Ruz, J.J. Integration of Constraint Programming and Evolution Programs: Application to Channel Routing. 11th Int. Conf. on Industrial Applications of Artificial Intelligence. LNAI 1415, Springer-Verlag (1998) 448–459.Google Scholar
- 7.Ruiz-Andino A. CSOS User's manual. Tech. Report No. 73.98. Department of Computer Science. Universidad Complutense de Madrid, (1998).Google Scholar
- 9.Wallace, M.: Constraints in Planing, Scheduling and Placement Problems. Constraint Programming, Springer-Verlag (1994).Google Scholar