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research
A theoretical and empirical study on unbiased boundary-extended crossover for real-valued representation
Authors
Alberto Moraglio
Beyer
+50 more
Beyer
Byung-Ro Moon
Chandra
Chen
Chu
Das
Dasgupta
Deb
Deb
Deb
Goldberg
Guanqi
Hansen
Herrera
Herrera
Herrera
Howell
Hu
Huang
Ibáñez
Igel
Juang
Jägersküpper
Kang
Kita
Kramer
Krohling
Leung
Li
Lozano
Mezura-Montes
Michalewicz
Mühlenbein
Peng
Petridis
Qi
Qi
Reed
Ripon
Socha
Someya
Storn
Sun
Tsutsui
Tu
Wei
Yong-Hyuk Kim
Yourim Yoon
Yue
Zhang
Publication date
1 January 2012
Publisher
'Elsevier BV'
Doi
Cite
Abstract
Copyright © 2012 Elsevier. NOTICE: this is the author’s version of a work that was accepted for publication in Information Sciences. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Information Sciences Vol. 183 Issue 1 (2012), DOI: 10.1016/j.ins.2011.07.013We present a new crossover operator for real-coded genetic algorithms employing a novel methodology to remove the inherent bias of pre-existing crossover operators. This is done by transforming the topology of the hyper-rectangular real space by gluing opposite boundaries and designing a boundary extension method for making the fitness function smooth at the glued boundary. We show the advantages of the proposed crossover by comparing its performance with those of existing ones on test functions that are commonly used in the literature, and a nonlinear regression on a real-world dataset
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