AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS
Autor(a) principal: | |
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Data de Publicação: | 2014 |
Outros Autores: | , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Pesquisa operacional (Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382014000200143 |
Resumo: | Random key genetic algorithms are heuristic methods for solving combinatorial optimization problems. They represent solutions as vectors of randomly generated real numbers, the so-called random keys. A deterministic algorithm, called a decoder, takes as input a vector of random keys and associates with it a feasible solution of the combinatorial optimization problem for which an objective value or fitness can be computed. We compare three types of random-key genetic algorithms: the unbiased algorithm of Bean (1994); the biased algorithm of Gonçalves and Resende (2010); and a greedy version of Bean's algorithm on 12 instances from four types of covering problems: general-cost set covering, Steiner triple covering, general-cost set k -covering, and unit-cost covering by pairs. Experiments are run to construct runtime distributions for 36 heuristic/instance pairs. For all pairs of heuristics, we compute probabilities that one heuristic is faster than the other on all 12 instances. The experiments show that, in 11 of the 12 instances, the greedy version of Bean's algorithm is faster than Bean's original method and that the biased variant is faster than both variants of Bean's algorithm. |
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AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMSgenetic algorithmbiased random-key genetic algorithmrandom keyscombinatorial optimizationheuristicsmetaheuristicsexperimental algorithmsRandom key genetic algorithms are heuristic methods for solving combinatorial optimization problems. They represent solutions as vectors of randomly generated real numbers, the so-called random keys. A deterministic algorithm, called a decoder, takes as input a vector of random keys and associates with it a feasible solution of the combinatorial optimization problem for which an objective value or fitness can be computed. We compare three types of random-key genetic algorithms: the unbiased algorithm of Bean (1994); the biased algorithm of Gonçalves and Resende (2010); and a greedy version of Bean's algorithm on 12 instances from four types of covering problems: general-cost set covering, Steiner triple covering, general-cost set k -covering, and unit-cost covering by pairs. Experiments are run to construct runtime distributions for 36 heuristic/instance pairs. For all pairs of heuristics, we compute probabilities that one heuristic is faster than the other on all 12 instances. The experiments show that, in 11 of the 12 instances, the greedy version of Bean's algorithm is faster than Bean's original method and that the biased variant is faster than both variants of Bean's algorithm.Sociedade Brasileira de Pesquisa Operacional2014-08-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382014000200143Pesquisa Operacional v.34 n.2 2014reponame:Pesquisa operacional (Online)instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)instacron:SOBRAPO10.1590/0101-7438.2014.034.02.0143info:eu-repo/semantics/openAccessGonçalves,José FernandoResende,Mauricio G.C.Toso,Rodrigo F.eng2015-10-09T00:00:00Zoai:scielo:S0101-74382014000200143Revistahttp://www.scielo.br/popehttps://old.scielo.br/oai/scielo-oai.php||sobrapo@sobrapo.org.br1678-51420101-7438opendoar:2015-10-09T00:00Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO)false |
dc.title.none.fl_str_mv |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
title |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
spellingShingle |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS Gonçalves,José Fernando genetic algorithm biased random-key genetic algorithm random keys combinatorial optimization heuristics metaheuristics experimental algorithms |
title_short |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
title_full |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
title_fullStr |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
title_full_unstemmed |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
title_sort |
AN EXPERIMENTAL COMPARISON OF BIASED AND UNBIASED RANDOM-KEY GENETIC ALGORITHMS |
author |
Gonçalves,José Fernando |
author_facet |
Gonçalves,José Fernando Resende,Mauricio G.C. Toso,Rodrigo F. |
author_role |
author |
author2 |
Resende,Mauricio G.C. Toso,Rodrigo F. |
author2_role |
author author |
dc.contributor.author.fl_str_mv |
Gonçalves,José Fernando Resende,Mauricio G.C. Toso,Rodrigo F. |
dc.subject.por.fl_str_mv |
genetic algorithm biased random-key genetic algorithm random keys combinatorial optimization heuristics metaheuristics experimental algorithms |
topic |
genetic algorithm biased random-key genetic algorithm random keys combinatorial optimization heuristics metaheuristics experimental algorithms |
description |
Random key genetic algorithms are heuristic methods for solving combinatorial optimization problems. They represent solutions as vectors of randomly generated real numbers, the so-called random keys. A deterministic algorithm, called a decoder, takes as input a vector of random keys and associates with it a feasible solution of the combinatorial optimization problem for which an objective value or fitness can be computed. We compare three types of random-key genetic algorithms: the unbiased algorithm of Bean (1994); the biased algorithm of Gonçalves and Resende (2010); and a greedy version of Bean's algorithm on 12 instances from four types of covering problems: general-cost set covering, Steiner triple covering, general-cost set k -covering, and unit-cost covering by pairs. Experiments are run to construct runtime distributions for 36 heuristic/instance pairs. For all pairs of heuristics, we compute probabilities that one heuristic is faster than the other on all 12 instances. The experiments show that, in 11 of the 12 instances, the greedy version of Bean's algorithm is faster than Bean's original method and that the biased variant is faster than both variants of Bean's algorithm. |
publishDate |
2014 |
dc.date.none.fl_str_mv |
2014-08-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382014000200143 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S0101-74382014000200143 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/0101-7438.2014.034.02.0143 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
publisher.none.fl_str_mv |
Sociedade Brasileira de Pesquisa Operacional |
dc.source.none.fl_str_mv |
Pesquisa Operacional v.34 n.2 2014 reponame:Pesquisa operacional (Online) instname:Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) instacron:SOBRAPO |
instname_str |
Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
instacron_str |
SOBRAPO |
institution |
SOBRAPO |
reponame_str |
Pesquisa operacional (Online) |
collection |
Pesquisa operacional (Online) |
repository.name.fl_str_mv |
Pesquisa operacional (Online) - Sociedade Brasileira de Pesquisa Operacional (SOBRAPO) |
repository.mail.fl_str_mv |
||sobrapo@sobrapo.org.br |
_version_ |
1750318017743421440 |