Challenging heuristics: Evolving binary constraint satisfaction problems uri icon


  • In computer science it is a common practice to evaluate the performance of algorithms using a set of benchmark or randomly generated instances. However, following that approach, the weaknesses of the algorithms may not be exposed. This work is the first phase of research project on coevolution of solutions methods versus problem instances. The goal of study is to generate a method to find difficult to solve problem instances capable of challenging the solution methods or algorithms under analysis, helping to discover opportunities for improvement. An evolutionary model is proposed to find hard binary constraint satisfaction problem instances for different variable ordering heuristics. We characterize the search space by generating random instances with different values for the constraint density and tightness. For all the heuristics, the most difficult problems are located in the same region of the space near to the phase transition. However, there are certain regions of the search space where a heuristic dominates the others, especially where the problems are solvable. Finally, we compare the hardest instances found during the search space exploration with the outcome instances of the evolutionary model. The results show that evolved instances are harder to solve than the ones randomly generated. © 2012 ACM.

Publication date

  • January 1, 2012