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		<doi>10.1016/j.asoc.2020.106760</doi>
		<issn>1568-4946</issn>
		<issn>1872-9681</issn>
		<citationkey>SantiagoJúniorÖzcaCarv:2020:HyBaRe</citationkey>
		<title>Hyper-Heuristics based on reinforcement learning, balanced heuristic selection and group decision acceptance</title>
		<year>2020</year>
		<month>Dec.</month>
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		<author>Santiago Júnior, Valdivino Alexandre de,</author>
		<author>Özcan, Ender,</author>
		<author>Carvalho, Vinicius Renan de,</author>
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		<group>LABAC-COCTE-INPE-MCTIC-GOV-BR</group>
		<affiliation>Instituto Nacional de Pesquisas Espaciais (INPE)</affiliation>
		<affiliation>University of Nottingham</affiliation>
		<affiliation>Universidade de São Paulo (USP)</affiliation>
		<electronicmailaddress>valdivino.santiago@inpe.br</electronicmailaddress>
		<electronicmailaddress>Ender.Ozcan@nottingham.ac.uk</electronicmailaddress>
		<electronicmailaddress>vrcarvalho@usp.br</electronicmailaddress>
		<journal>Applied Soft Computing Journal</journal>
		<volume>97</volume>
		<secondarymark>A2_INTERDISCIPLINAR A2_ENGENHARIAS_IV A2_ENGENHARIAS_III A2_CIÊNCIA_DA_COMPUTAÇÃO B1_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B1_ENGENHARIAS_II B1_BIOTECNOLOGIA</secondarymark>
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		<pages>e106760</pages>
		<keywords>Hyper-heuristic, Reinforcement learning, Balanced heuristic selection, Group decision-making, Multi-objective evolutionary algorithms, Multi-objective optimisation.</keywords>
		<abstract>In this paper, we introduce a multi-objective selection hyper-heuristic approach combining Reinforcement Learning, (meta)heuristic selection, and group decision-making as acceptance methods, referred to as Hyper-Heuristic based on Reinforcement LearnIng, Balanced Heuristic Selection and Group Decision AccEptance (HRISE), controlling a set of Multi-Objective Evolutionary Algorithms (MOEAs) as Low-Level (meta)Heuristics (LLHs). Along with the use of multiple MOEAs, we believe that having a robust LLH selection method as well as several move acceptance methods at our disposal would lead to an improved general-purpose method producing most adequate solutions to the problem instances across multiple domains. We present two learning hyper-heuristics based on the HRISE framework for multi-objective optimisation, each embedding a group decision-making acceptance method under a different rule: majority rule (HRISE_M) and responsibility rule (HRISE_R). A third hyper-heuristic is also defined where both a random LLH selection and a random move acceptance strategy are used. We also propose two variants of the late acceptance method and a new quality indicator supporting the initialisation of selection hyper-heuristics using low computational budget. An extensive set of experiments were performed using 39 multi-objective problem instances from various domains where 24 are from four different benchmark function classes, and the remaining 15 instances are from four different real-world problems. The cross-domain search performance of the proposed learning hyperheuristics indeed turned out to be the best, particularly HRISE_R, when compared to three other selection hyper-heuristics, including a recently proposed one, and all low-level MOEAs each run in isolation.</abstract>
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		<language>en</language>
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