GRASP - evolution for constraint satisfaction problems

Authors:
Manuel Cebrián;Iván Dotú
Affiliations:
Universidad Autónoma de Madrid, Madrid, Spain;Universidad Autónoma de Madrid, Madrid, Spain
Venue:
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Year:
2006

Citing 4
Cited 1

Evolutionary algorithms and constraint satisfaction: definitions, survey, methodology, and research directions

Theoretical aspects of evolutionary computing
Random Constraint Satisfaction: A More Accurate Picture

Constraints
Reactive GRASP: An Application to a Matrix Decomposition Problem in TDMA Traffic Assignment

INFORMS Journal on Computing
Comparing evolutionary algorithms on binary constraint satisfaction problems

IEEE Transactions on Evolutionary Computation

Survey: A survey of repair methods used as constraint handling techniques in evolutionary algorithms

Computer Science Review

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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 ofefficiency. Our algorithm is conceptual and simple, featuring a GRASP-like (GRASP stands for Greedy Randomized Adaptive Search Procedure) mechanism for genotype-to-phenotype mapping, and without considering any specific knowledge of the problem. Therefore, we provide a simple algorithm that harnesses generality while boosting performance.