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Output details

11 - Computer Science and Informatics

University of Portsmouth

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Output 12 of 42 in the submission

Article title

Autonomous Virulence Adaptation Improves Coevolutionary Optimization

Type

D - Journal article

DOI

10.1109/TEVC.2010.2073471

Title of journal

IEEE Transactions on Evolutionary Computation

Article number

Volume number

Issue number

First page of article

215

ISSN of journal

1941-0026

Year of publication

2011

URL

http://eprints.port.ac.uk/6077/

Number of additional authors

Additional information

<22>The paper reports, in a leading AI journal, the problem of population disengagement in co-evolutionary optimisation frameworks. A novel technique, autonomous virulence adaptation (AVA), is presented for tuning the virulence parameter of 2-population competitive genetic algorithm (CGA) systems. Using two classic complex case studies, AVA is shown to offer quality solutions and a significantly reduced computational overhead.

Interdisciplinary

Cross-referral requested

Research group

None

Citation count

Proposed double-weighted

Double-weighted statement

Reserve for a double-weighted output

Non-English

English abstract