Conference Proceeding Article
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.
Artificial Intelligence and Robotics | Operations Research, Systems Engineering and Industrial Engineering
Intelligent Systems and Decision Analytics
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
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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. Research Collection School Of Information Systems.
Available at: http://ink.library.smu.edu.sg/sis_research/2666
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