Publication Type

Conference Proceeding Article

Publication Date

7-2014

Abstract

Memetic search is well known as one of the state-of-the-art metaheuristics for finding high-quality solutions to NP-hard problems. Its performance is often attributable to appropriate design, including the choice of its operators. In this paper, we propose a Markov Decision Process model for the selection of crossover operators in the course of the evolutionary search. We solve the proposed model by a Q-learning method. We experimentally verify the efficacy of our proposed approach on the benchmark instances of Quadratic Assignment Problem.

Discipline

Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics

Publication

GECCO Comp '14: Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, July 12-16, 2014, Vancouver, BC, Canada

First Page

193

Last Page

194

ISBN

9781450328814

Identifier

10.1145/2598394.2598451

Publisher

ACM

City or Country

New York

Copyright Owner and License

LARC

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL

http://doi.org/10.1145/2598394.2598451