@Article{SantiagoJúniorÖzcaCarv:2020:HyBaRe,
author = "Santiago J{\'u}nior, Valdivino Alexandre de and {\"O}zcan, Ender
and Carvalho, Vinicius Renan de",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {University
of Nottingham} and {Universidade de S{\~a}o Paulo (USP)}",
title = "Hyper-Heuristics based on reinforcement learning, balanced
heuristic selection and group decision acceptance",
journal = "Applied Soft Computing Journal",
year = "2020",
volume = "97",
pages = "e106760",
month = "Dec.",
keywords = "Hyper-heuristic, Reinforcement learning, Balanced heuristic
selection, Group decision-making, Multi-objective evolutionary
algorithms, Multi-objective optimisation.",
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.",
doi = "10.1016/j.asoc.2020.106760",
url = "http://dx.doi.org/10.1016/j.asoc.2020.106760",
issn = "1568-4946 and 1872-9681",
language = "en",
targetfile = "valdivino_hyper.pdf",
urlaccessdate = "2024, Mar. 28"
}