Biased random-key genetic algorithms for combinatorial optimization

• Tipo: Artigo em Revista Científica Internacional
• Data: 2011

Autores

Jose Fernando Goncalves (Autor) FEP; Mauricio G C Resende (Autor) Outra

Revista

Título: Journal of Heuristics

Vol. 17 Nº 5

Páginas: 487-525

ISSN: 1381-1231

Editora: Springer Nature

Indexação

ISI Web of Knowledge - 191 Citações

ISI Web of Science

Scopus - 266 Citações

Classificação Científica

CORDIS: Ciências Sociais > Economia > Estudos de gestão

FOS: Ciências exactas e naturais > Ciências da computação e da informação

Outras Informações

ID Authenticus: P-002-M6M

DOI: 10.1007/s10732-010-9143-1

Abstract (EN): Random-key genetic algorithms were introduced by Bean (ORSA J. Comput. 6:154-160, 1994) for solving sequencing problems in combinatorial optimization. Since then, they have been extended to handle a wide class of combinatorial optimization problems. This paper presents a tutorial on the implementation and use of biased random-key genetic algorithms for solving combinatorial optimization problems. Biased random-key genetic algorithms are a variant of random-key genetic algorithms, where one of the parents used for mating is biased to be of higher fitness than the other parent. After introducing the basics of biased random-key genetic algorithms, the paper discusses in some detail implementation issues, illustrating the ease in which sequential and parallel heuristics based on biased random-key genetic algorithms can be developed. A survey of applications that have recently appeared in the literature is also given.

Idioma: Inglês

Tipo (Avaliação Docente): Científica

Nº de páginas: 39