Publication Type
Conference Proceeding Article
Version
publishedVersion
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
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
Citation
HANDOKO, Stephanus Daniel; Nguyen, Duc Thien; YUAN, Zhi; and LAU, Hoong Chuin.
Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem. (2014). 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. 193-194.
Available at: https://ink.library.smu.edu.sg/sis_research/2666
Copyright Owner and License
LARC
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.
Additional URL
http://doi.org/10.1145/2598394.2598451
Included in
Artificial Intelligence and Robotics Commons, Operations Research, Systems Engineering and Industrial Engineering Commons