Biased random-key genetic algorithms for combinatorial optimization

• Type: Article in International Scientific Journal
• Date: 2011

Authors

Jose Fernando Goncalves (Author) FEP; Mauricio G C Resende (Author) Other

Journal

Title: Journal of Heuristics

Vol. 17 No. 5

Pages: 487-525

ISSN: 1381-1231

Publisher: Springer Nature

Indexing

ISI Web of Knowledge - 191 Citations

ISI Web of Science

Scopus - 266 Citations

Scientific classification

CORDIS: Social sciences > Economics > Management studies

FOS: Natural sciences > Computer and information sciences

Other information

Authenticus ID: 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.

Language: English

Type (Professor's evaluation): Scientific

No. of pages: 39