Evolutionary Computation in Constraint Satisfaction and Machine Learning
Jano I. Van Hemert · 2001
Introduction At rst sight the two problem areas constraint satisfaction and machine learning do not appear to be similar. The rst has a clear denition of its problem domain and a crisp denition of solutions. The second is a much broader dened problem domain, which leads to many objectives to be solved. Many research areas have focused there attention on constraint satisfaction, operating research, ant colonies, evolutionary computation and, most notable, constraint programming. Although the problems that are being studied share the same goal, which is to satisfy a set of constraints, their precise denition varies. Among these problems we nd numerous well known ones such as, k-graph colouring, 3-sat and n-queens. For any of these problems we can transform them to a binary constraint satisfaction problem without loss of generality. This holds for any nite constraint satisfaction problem, and it means that we can solve a probl