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A hybrid evolutionary approach for solving constrained optimization problems over finite domains

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4 Author(s)
Ruiz-Andino, A. ; Univ. Complutense de Madrid, Spain ; Araujo, L. ; Saenz, F. ; Ruz, J.J.

A novel approach for the integration of evolution programs and constraint-solving techniques over finite domains is presented. This integration provides a problem-independent optimization strategy for large-scale constrained optimization problems over finite domains. In this approach, genetic operators are based on an arc-consistency algorithm, and chromosomes are arc-consistent portions of the search space of the problem. The paper describes the main issues arising in this integration: chromosome representation and evaluation, selection and replacement strategies, and the design of genetic operators. We also present a parallel execution model for a distributed memory architecture of the previous integration. We have adopted a global parallelization approach that preserves the properties, behavior, and fundamentals of the sequential algorithm. Linear speedup is achieved since genetic operators are coarse grained as they perform a search in a discrete space carrying out arc consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:4 ,  Issue: 4 )

Date of Publication:

Nov 2000

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