01488nas a2200217 4500008004100000020001800041245013700059210006900196260002800265300001300293520075400306653002801060653002701088653002801115653002901143100001601172700001401188700001601202700001301218856003901231 2004 eng d a3-540-21367-800aA Study into Ant Colony Optimization, Evolutionary Computation and Constraint Programming on Binary Constraint Satisfaction Problems0 aStudy into Ant Colony Optimization Evolutionary Computation and bSpringer-Verlag, Berlin a114--1233 aWe compare two heuristic approaches, evolutionary computation and ant colony optimisation, and a complete tree-search approach, constraint programming, for solving binary constraint satisfaction problems. We experimentally show that, if evolutionary computation is far from being able to compete with the two other approaches, ant colony optimisation nearly always succeeds in finding a solution, so that it can actually compete with constraint programming. The resampling ratio is used to provide insight into heuristic algorithms performances. Regarding efficiency, we show that if constraint programming is the fastest when instances have a low number of variables, ant colony optimisation becomes faster when increasing the number of variables.10aant colony optimisation10aconstraint programming10aconstraint satisfaction10aevolutionary computation1 aHemert, J I1 aSolnon, C1 aGottlieb, J1 aRaidl, G uhttp://research.nesc.ac.uk/node/22