Improving Evolutionary Algorithms for Efficient Constraint Satisfaction

Hard or large-scale constraint satisfaction and optimization problems, occur widely in artiicial intelligence and operations research. These problems are often diicult to solve with global search methods, but many of them can be eeciently solved by local search methods. Evolutionary algorithms are local search methods which have considerable success in tackling diicult, or ill-deened optimization problems. In contrast they have not been so successful in tackling constraint satisfaction problems. Other local search methods, in particular GENET and EGENET are designed speciically for constraint satisfaction problems, and have demonstrated remarkable success in solving hard examples of these problems. In this paper we examine how we can transfer the mechanisms that were so successful in (E)GENET to evolutionary algorithms, in order to tackle constraint satisfaction algorithms eeciently. An empirical comparison of our evolutionary algorithm improved by mechanisms from EGENET and shows how it can markedly improve on the eeciency of EGENET in solving certain hard instances of constraint satisfaction problems.