Comparing the performance of evolutionary algorithms for permutation constraint satisfaction

Authors:
Luis de-Marcos;Antonio García;Eva García;José-Amelio Medina;Salvador Otón
Affiliations:
University of Alcalá, Alcalá de Henares, Spain;University of Alcalá, Alcalá de Henares, Spain;University of Alcalá, Alcalá de Henares, Spain;University of Alcalá, Alcalá de Henares, Spain;University of Alcalá, Alcalá de Henares, Spain
Venue:
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
Year:
2011

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Abstract

This paper presents a systematic comparison of canonical versions of two evolutionary algorithms, namely Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), for permutation constraint satisfaction (permut-CSP). Permut-CSP is first characterized and a test case is designed. Agents are then presented, tuned and compared. They are also compared with two classic methods (A* and hill climbing). Results show that PSO statistically outperforms all other agents, suggesting that canonical implementations of this technique return the best trade-off between performance and development cost for our test case.