Parallel Evolutionary Optimization with Constraint Propagation.
A. Ruiz-Andino, L. Araujo,  J. J. Ruz, F. Sez.
PPSN 98. Lecture Notes on Computer Science 1498, Springer-Verlag, pg 270-279.

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.