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Using deep Q-network for selection hyper-heuristics

Authors:

Augusto Dantas,

Alexander Fiabane do Rego,

Aurora PozoAuthors Info & Claims

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

Pages 1488 - 1492

https://doi.org/10.1145/3449726.3463187

Published: 08 July 2021 Publication History

Abstract

Hyper-Heuristics is an active research field that aims to automatically select (or generate) the best low-level heuristic in each step of the search process. This work investigates a Hyper-Heuristic with a Deep Q-Network (DQN) selection strategy and compares it with two state-of-the-art approaches, namely the Dynamic MAB and the Fitness-Rate-Rank MAB. The experiments conducted on two domains from the HyFlex framework showed that the DQN approach outperformed the others on the Vehicle Routing Problem and was competitive on the Traveling Salesman Problem. This indicates that the DQN is a robust selection strategy that is less sensitive to the domain than the MAB based approaches.

References

[1]

Peter Auer, Nicolo Cesa-Bianchi, and Paul Fischer. 2002. Finite-time analysis of the multiarmed bandit problem. Machine learning 47, 2 (2002), 235--256.

Digital Library

[2]

Christian Blum, Jakob Puchinger, Günther R. Raidl, and Andrea Roli. 2011. Hybrid metaheuristics in combinatorial optimization: A survey. Applied Soft Computing 11, 6 (2011), 4135 -- 4151.

Digital Library

[3]

Edmund K. Burke, Matthew Hyde, Graham Kendall, Gabriela Ochoa, Ender Özcan, and John R. Woodward. 2010. A Classification of Hyper-heuristic Approaches. Springer US, Boston, MA, 449--468.

[4]

Luis DaCosta, Alvaro Fialho, Marc Schoenauer, and Michèle Sebag. 2008. Adaptive Operator Selection with Dynamic Multi-Armed Bandits. In Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation (GECCO '08). Association for Computing Machinery, New York, NY, USA, 913--920.

Digital Library

[5]

Carolina P. de Almeida, Richard A. Gonçalves, Sandra M. Venske, Ricardo Lüders, and Myriam Regattieri Delgado. 2018. Multi-armed Bandit Based Hyper-Heuristics for the Permutation Flow Shop Problem. In 7th Brazilian Conference on Intelligent Systems, BRACIS 2018, São Paulo, Brazil, October 22-25, 2018. IEEE Computer Society, 139--144.

[6]

John H. Drake, Ahmed Kheiri, Ender Özcan, and Edmund K. Burke. 2020. Recent advances in selection hyper-heuristics. European Journal of Operational Research 285, 2 (2020), 405--428.

[7]

A. S. Ferreira, R. A. Gonçalves, and A. Pozo. 2017. A Multi-Armed Bandit selection strategy for Hyper-heuristics. In 2017 IEEE Congress on Evolutionary Computation (CEC). 525--532.

Digital Library

[8]

Álvaro Fialho. 2010. Adaptive Operator Selection for Optimization. Theses. Université Paris Sud - Paris XI. https://tel.archives-ouvertes.fr/tel-00578431

[9]

Stephanus Daniel Handoko, Duc Thien Nguyen, Zhi Yuan, and Hoong Chuin Lau. 2014. Reinforcement Learning for Adaptive Operator Selection in Memetic Search Applied to Quadratic Assignment Problem. In Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation (GECCO Comp '14). Association for Computing Machinery, New York, NY, USA, 193--194.

Digital Library

[10]

K. Li, Á. Fialho, S. Kwong, and Q. Zhang. 2014. Adaptive Operator Selection With Bandits for a Multiobjective Evolutionary Algorithm Based on Decomposition. IEEE Transactions on Evolutionary Computation 18, 1 (2014), 114--130.

[11]

G. Ochoa, M. Hyde, T. Curtois, J.A. Vazquez-Rodriguez, J. Walker, M. Gendreau, G. Kendall, B. McCollum, A.J. Parkes, S. Petrovic, and E.K. Burke. 2012. HyFlex: A Benchmark Framework for Cross-domain Heuristic Search. 7245 (2012), 136--147.

[12]

Martin L. Puterman. 1990. Chapter 8 Markov Decision Processes. In Handbooks in Operations Research and Management Science. Stochastic Models, Vol. 2. Elsevier, 331--434.

[13]

Jorge A. Soria-Alcaraz, Gabriela Ochoa, Marco A. Sotelo-Figeroa, and Edmund K. Burke. 2017. A methodology for determining an effective subset of heuristics in selection hyper-heuristics. European Journal of Operational Research 260, 3 (2017), 972--983.

[14]

Richard S. Sutton and Andrew G. Barto. 2018. Reinforcement Learning, Second Edition: An Introduction. MIT Press.

Digital Library

[15]

Teck-Hou Teng, Stephanus Daniel Handoko, and Hoong Chuin Lau. 2016. Self-Organizing Neural Network for Adaptive Operator Selection in Evolutionary Search. In Learning and Intelligent Optimization (Lecture Notes in Computer Science), Paola Festa, Meinolf Sellmann, and Joaquin Vanschoren (Eds.). Springer International Publishing, Cham, 187--202.

[16]

Christopher J. C. H. Watkins and Peter Dayan. 1992. Q-Learning. Machine Learning 8, 3 (May 1992), 279--292.

Digital Library

[17]

D. H. Wolpert and W. G. Macready. 1997. No Free Lunch Theorems for Optimization. IEEE Transactions on Evolutionary Computation 1, 1 (April 1997), 67--82.

Digital Library

Cited By

Li CWei XWang JWang SZhang S(2024)A review of reinforcement learning based hyper-heuristicsPeerJ Computer Science10.7717/peerj-cs.214110(e2141)Online publication date: 28-Jun-2024
https://doi.org/10.7717/peerj-cs.2141
Bouazza W(2024)Machine Learning-Based Hyper-Heuristics: A Clear InsightProceedings of the 2024 7th International Conference on Computational Intelligence and Intelligent Systems10.1145/3708778.3708783(29-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3708778.3708783
Pei JLiu JMei YLi XHandl J(2024)Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection FrameworkProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654062(1017-1025)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654062
Show More Cited By

Index Terms

Using deep Q-network for selection hyper-heuristics
1. Computing methodologies
  1. Artificial intelligence
    1. Search methodologies
  2. Machine learning
    1. Learning paradigms
      1. Reinforcement learning

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Published In

cover image ACM Conferences

GECCO '21: Proceedings of the Genetic and Evolutionary Computation Conference Companion

July 2021

2047 pages

ISBN:9781450383516

DOI:10.1145/3449726

Editor:
Francisco Chicano
University of Malaga
,
General Chair:
Krzysztof Krawiec
Poznan University of Technology

Copyright © 2021 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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SIGEVO: ACM Special Interest Group on Genetic and Evolutionary Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 08 July 2021

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CAPES
CNPq

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GECCO '21

Sponsor:

SIGEVO

GECCO '21: Genetic and Evolutionary Computation Conference

July 10 - 14, 2021

Lille, France

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Li CWei XWang JWang SZhang S(2024)A review of reinforcement learning based hyper-heuristicsPeerJ Computer Science10.7717/peerj-cs.214110(e2141)Online publication date: 28-Jun-2024
https://doi.org/10.7717/peerj-cs.2141
Bouazza W(2024)Machine Learning-Based Hyper-Heuristics: A Clear InsightProceedings of the 2024 7th International Conference on Computational Intelligence and Intelligent Systems10.1145/3708778.3708783(29-37)Online publication date: 22-Nov-2024
https://dl.acm.org/doi/10.1145/3708778.3708783
Pei JLiu JMei YLi XHandl J(2024)Learning from Offline and Online Experiences: A Hybrid Adaptive Operator Selection FrameworkProceedings of the Genetic and Evolutionary Computation Conference10.1145/3638529.3654062(1017-1025)Online publication date: 14-Jul-2024
https://dl.acm.org/doi/10.1145/3638529.3654062
Udomkasemsub OSirinaovakul BAchalakul T(2023)PHH: Policy-Based Hyper-Heuristic With Reinforcement LearningIEEE Access10.1109/ACCESS.2023.327795311(52026-52049)Online publication date: 2023
https://doi.org/10.1109/ACCESS.2023.3277953
Yi WQu RJiao L(2023)Automated algorithm design using proximal policy optimisation with identified featuresExpert Systems with Applications10.1016/j.eswa.2022.119461216(119461)Online publication date: Apr-2023
https://doi.org/10.1016/j.eswa.2022.119461

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