@Article{BarbosaSenn:2017:ApCoDe,
author = "Barbosa, Eduardo Batista de Moraes and Senne, Edson Luiz
Fran{\c{c}}a",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and
{Universidade Estadual Paulista (UNESP)}",
title = "Improving the fine-tuning of metaheuristics: an approach combining
design of experiments and racing algorithms",
journal = "Journal of Optimization",
year = "2017",
volume = "2017",
pages = "1--7",
keywords = "Metaheuristics, Fine-tuning, Combinatorial optimization,
Nonparametric statistics.",
abstract = "Usually, metaheuristic algorithms are adapted to a large set of
problems by applying few modifications on parameters for each
specific case. However, this flexibility demands a huge effort to
correctly tune such parameters. Therefore, the tuning of
metaheuristics arises as one of the most important challenges in
the context of research of these algorithms.Thus, this paper aims
to present a methodology combining Statistical andArtificial
Intelligencemethods in the fine-tuning ofmetaheuristics.Thekey
idea is a heuristic method, called Heuristic Oriented Racing
Algorithm (HORA), which explores a search space of parameters
looking for candidate configurations close to a promising
alternative. To confirm the validity of this approach, we present
a case study for finetuning two distinct metaheuristics: Simulated
Annealing (SA) and Genetic Algorithm (GA), in order to solve the
classical traveling salesman problem. The results are compared
considering the same metaheuristics tuned through a racing method.
Broadly, the proposed approach proved to be effective in terms of
the overall time of the tuning process. Our results reveal that
metaheuristics tuned by means of HORA achieve, with much less
computational effort, similar results compared to the case when
they are tuned by the other fine-tuning approach.",
doi = "10.1155/2017/8042436",
url = "http://dx.doi.org/10.1155/2017/8042436",
issn = "2356-752X",
label = "lattes: 8920905542032636 1 BarbosaSenn:2017:ApCoDe",
language = "en",
targetfile = "barbosa_improving.pdf",
urlaccessdate = "05 jun. 2024"
}