@InProceedings{BarbosaSenn:2017:HeOpMe,
author = "Barbosa, Eduardo Batista de Moraes and Senne, Edson Luiz
Fran{\c{c}}a",
affiliation = "{Instituto Nacional de Pesquisas Espaciais (INPE)} and {Instituto
Nacional de Pesquisas Espaciais (INPE)}",
title = "A heuristic for optimization of metaheuristics by means of
statistical methods",
booktitle = "Proceedings...",
year = "2017",
pages = "203--210",
organization = "International Conference on Operations Research and Enterprise
Systems, 6. (ICORES)",
keywords = "Metaheuristics, Fine-tuning, Combinatorial optimization,
Nonparametric statistics.",
abstract = "The fine-tuning of the algorithms parameters, specially, in
metaheuristics, is not always trivial and often is performed by ad
hoc methods according to the problem under analysis. Usually,
incorrect settings influence both in the algorithms performance,
as in the quality of solutions. The tuning of metaheuristics
requires the use of innovative methodologies, usually interesting
to different research communities. In this context, this paper
aims to contribute to the literature by presenting a methodology
combining Statistical and Artificial Intelligence methods in the
fine-tuning of metaheuristics. The key idea is a heuristic method,
called Heuristic Oriented Racing Algorithm (HORA), which explores
a search space of parameters, looking for candidate configurations
near of a promising alternative, and consistently finds good
settings for different metaheuristics. To confirm the validity of
this approach, we present a case study for fine-tuning two
distinct metaheuristics: Simulated Annealing (SA) and Genetic
Algorithm (GA), in order to solve a classical task scheduling
problem. The results of the proposed approach are compared with
results yielded by the same metaheuristics tuned through different
strategies, such as the brute-force and racing. Broadly, the
proposed method proved to be effective in terms of the overall
time of the tuning process. Our results from experimental studies
reveal that metaheuristics tuned by means of HORA reach the same
good results than when tuned by the other time-consuming
fine-tuning approaches. Therefore, from the results presented in
this study it is concluded that HORA is a promising and powerful
tool for the fine-tuning of different metaheuristics, mainly when
the overall time of tuning process is considered.",
conference-location = "Porto, Portugal",
conference-year = "23-25 Feb.",
doi = "10.5220/0006106402030210",
url = "http://dx.doi.org/10.5220/0006106402030210",
isbn = "978-989-758-218-9",
label = "lattes: 8920905542032636 1 BarbosaSenn:2017:HeOpMe",
language = "en",
targetfile = "barbosa_a heuristic.pdf",
urlaccessdate = "27 abr. 2024"
}