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%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2020/10.23.12.00
%2 sid.inpe.br/mtc-m21c/2020/10.23.12.00.29
%@issn 1568-4946
%@issn 1872-9681
%T Hyper-Heuristics based on reinforcement learning, balanced heuristic selection and group decision acceptance
%D 2020
%8 Dec.
%9 journal article
%A Santiago Jnior, Valdivino Alexandre de,
%A zcan, Ender,
%A Carvalho, Vinicius Renan de,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation University of Nottingham
%@affiliation Universidade de So Paulo (USP)
%@electronicmailaddress valdivino.santiago@inpe.br
%@electronicmailaddress Ender.Ozcan@nottingham.ac.uk
%@electronicmailaddress vrcarvalho@usp.br
%B Applied Soft Computing Journal
%V 97
%P e106760
%K Hyper-heuristic, Reinforcement learning, Balanced heuristic selection, Group decision-making, Multi-objective evolutionary algorithms, Multi-objective optimisation.
%X 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.
%@language en
%3 valdivino_hyper.pdf


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