Publication:
The effectiveness of hybrid negative correlation learning in evolutionary algorithm for combinatorial optimization problems

Issued Date

2011-12-01

Resource Type

Conference Paper

ISSN

2157362X
21573611

DOI

10.1109/IEEM.2011.6117963

Other identifier(s)

2-s2.0-84856546222

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

IEEE International Conference on Industrial Engineering and Engineering Management. (2011), 476-481

Suggested Citation

R. Sirovetnukul, P. Chutima, W. Wattanapornprom, P. Chongstitvatana The effectiveness of hybrid negative correlation learning in evolutionary algorithm for combinatorial optimization problems. IEEE International Conference on Industrial Engineering and Engineering Management. (2011), 476-481. doi:10.1109/IEEM.2011.6117963 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/11644

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Title

The effectiveness of hybrid negative correlation learning in evolutionary algorithm for combinatorial optimization problems

Author(s)

R. Sirovetnukul
 P. Chutima
 W. Wattanapornprom
 P. Chongstitvatana

Other Contributor(s)

Mahidol University
 Chulalongkorn University

Abstract

Most evolutionary algorithms optimize the information from good solutions found in the population. A selection method discards the below-average solutions assuming that they do not contribute any information to update the probabilistic models. This work develops an algorithm called Coincidence algorithm (COIN) which merges negative correlation learning into the optimization process. A knight's tour problem, one of NP-hard multimodal Hamiltonian path problems, is tested with COIN. The results show that COIN is a competitive algorithm in converging to better solutions and maintaining diverse solutions to solve combinatorial optimization problems. © 2011 IEEE.

Keyword(s)

Business, Management and Accounting
 Engineering

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/11644

Collections

Scopus 2011-2015