ACO for Maximal Constraint Satisfa tion Problems

Generating Random Solutions from a Constraint Satisfa tion Problem with Controlled Probability

Symmetry Breaking in Constraint Satisfa tion with Graph-Isomorphism: Comma-Free Codes

Lower Bounds for non-Boolean Constraint Satisfa tion Programs

Constraint Satisfa tion Programs Lars Engebretsen Dept. of Numeri al Analysis and Computer S ien e Royal Institute of Te hnology SE-100 44 Sto kholm, SWEDEN E-mail: enge nada.kth.se Abstra t. We show that the k-CSP problem over a nite Abelian group G annot be approximated within jGjk O(pk) , for any onstant > 0, unless P = NP. This lower bound mat hes well with the best known upper bound, jGjk ...

ACO for Maximal Constraint Satisfaction Problems

A CSP is formally defined as a triple (X,D,C), where X = {x1, . . . , xn} is the set of variables, D = {D1, . . . , Dn} is the set of domains which define the values a variable can assume and C = {C1, . . . , Cm} is the set of constraints among the variables. The CSP is a decision problem: a solution of the problem is a complete assignment which satisfies all the constraints (see [12] for an ov...

An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems

We introduce two reactive frameworks for dynamically adapting some parameters of an Ant Colony Optimization (ACO) algorithm. Both reactive frameworks use ACO to adapt parameters: pheromone trails are associated with parameter values; these pheromone trails represent the learnt desirability of using parameter values and are used to dynamically set parameters in a probabilistic way. The two frame...