Using self-learning and automatic tuning to improve the performance of sexual genetic algorithms for constraint satisfaction problems

Currently the parameters in a constraint solver are often selected by hand by experts in the field; these parameters might include the level of preprocessing to be used and the variable ordering heuristic. The efficient and automatic choice of a preprocessing level for a constraint solver is a step towards making constraint programming a more widely accessible technology. Self-learning sexual genetic algorithms are a new approach combining a self-learning mechanism with sexual genetic algorithms in order to suggest or predict a suitable solver configuration for large scale problems by learning from the same class of small scale problems. In this paper, Self-learning Sexual genetic algorithms are applied to create an automatic solver configuration mechanism for solving various constraint problems. The starting population of self-learning sexual genetic algorithms will be trained through experience on small instances. The experiments in this paper are a proof-of-concept for the idea of combining sexual genetic algorithms with a self-learning strategy to aid in parameter selection for constraint programming