A hybrid evolutionary approach for solving constrained optimization problems over finite domains
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 are-consistency algorithm, and chromosomes are are-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 are consistency. The implementation has been tested on a GRAY T3E multiprocessor using a complex constrained optimization problem.
evolution programs arc consistency constrained combinatorial optimization problems