Solving constraint satisfaction problems with heuristic-based evolutionary algorithms
Bart G. W. Craenen, Agoston Endre Eiben, Elena Marchiori · 2002
Evolutionary algorithms (EAs) for solving constraint satisfaction problems (CSPs) can be roughly divided into two classes: EAs with adaptive fitness functions and heuristic-based EAs. A.E. Eiben et al. (1998) compared effective EAs of the first class experimentally using a large set of benchmark instances consisting of randomly-generated binary CSPs. In this paper, we complete this comparison by performing the same experiments using three of the most effective heuristic-based EAs. The results of our experiments indicate that the three heuristic-based EAs have similar performances on random binary CSPs. Comparing these results with those of A.E. Eiben et al., we are able to identify the best EA for binary CSPs as the algorithm introduced by G. Dozier et al. (1994), which uses a heuristic as well as an adaptive fitness function.