Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem

Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem

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 sel...

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Main Authors: HANDOKO, Stephanus Daniel, Nguyen, Duc Thien, YUAN, Zhi, LAU, Hoong Chuin
Format: text
Language:English
Published: Institutional Knowledge at Singapore Management University 2014
Subjects:
Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
Online Access:https://ink.library.smu.edu.sg/sis_research/2666
https://ink.library.smu.edu.sg/context/sis_research/article/3666/viewcontent/ReinforcementLearningAdaptiveOperSelQAP_2014_GECCO.pdf
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Institution: Singapore Management University
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spelling sg-smu-ink.sis_research-36662016-12-15T06:23:08Z Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem HANDOKO, Stephanus Daniel Nguyen, Duc Thien YUAN, Zhi LAU, Hoong Chuin 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. 2014-07-01T07:00:00Z text application/pdf https://ink.library.smu.edu.sg/sis_research/2666 info:doi/10.1145/2598394.2598451 https://ink.library.smu.edu.sg/context/sis_research/article/3666/viewcontent/ReinforcementLearningAdaptiveOperSelQAP_2014_GECCO.pdf http://creativecommons.org/licenses/by-nc-nd/4.0/ Research Collection School Of Computing and Information Systems eng Institutional Knowledge at Singapore Management University Artificial Intelligence and Robotics Operations Research, Systems Engineering and Industrial Engineering
institution Singapore Management University
building SMU Libraries
continent Asia
country Singapore
Singapore
content_provider SMU Libraries
collection InK@SMU
language English
topic Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
spellingShingle Artificial Intelligence and Robotics
Operations Research, Systems Engineering and Industrial Engineering
HANDOKO, Stephanus Daniel
Nguyen, Duc Thien
YUAN, Zhi
LAU, Hoong Chuin
Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
description 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.
format text
author HANDOKO, Stephanus Daniel
Nguyen, Duc Thien
YUAN, Zhi
LAU, Hoong Chuin
author_facet HANDOKO, Stephanus Daniel
Nguyen, Duc Thien
YUAN, Zhi
LAU, Hoong Chuin
author_sort HANDOKO, Stephanus Daniel
title Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
title_short Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
title_full Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
title_fullStr Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
title_full_unstemmed Reinforcement learning for adaptive operator selection in memetic search applied to Quadratic Assignment Problem
title_sort reinforcement learning for adaptive operator selection in memetic search applied to quadratic assignment problem
publisher Institutional Knowledge at Singapore Management University
publishDate 2014
url https://ink.library.smu.edu.sg/sis_research/2666
https://ink.library.smu.edu.sg/context/sis_research/article/3666/viewcontent/ReinforcementLearningAdaptiveOperSelQAP_2014_GECCO.pdf
_version_ 1770572558742061056

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