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 advantages of larger populations,
as well as properties, behaviour, and theoretical studies of the sequential
algorithm. Moreover, high speedup is achieved since genetic operators
are
coarse-grained, 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.