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GRASP-Evolution for Constraint Satisfaction Problems ABSTRACT

By Manuel Cebrián and Escuela Politécnica

Abstract

There are several evolutionary approaches for solving random binary Constraint Satisfaction Problems (CSPs). In most of these strategies we find a complex use of information regarding the problem at hand. Here we present a hybrid Evolutionary Algorithm that outperforms previous approaches in terms of effectiveness and compares well in terms of efficiency. Our algorithm is conceptual and simple, featuring a GRASP-like (GRASP stands for Greedy Randomized Adaptive Search Procedure) mechanism for genotypeto-phenotype mapping, and without considering any specific knowledge of the problem. Therefore, we provide a simple algorithm that harnesses generality while boosting performance

Topics: Algorithms, Performance, Experimentation Keywords Evolutionary Combinatorial Optimization, Random Binary CSPs, Constraint Handling, Heuristics, Hybridization
Year: 2009
OAI identifier: oai:CiteSeerX.psu:10.1.1.135.7611
Provided by: CiteSeerX
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