Co-evolutionary self-adaptive differential evolution with a uniform-distribution update rule

Amin Nobakhti; Hong Wang

doi:10.1109/ISIC.2006.285624

Co-evolutionary self-adaptive differential evolution with a uniform-distribution update rule

Amin Nobakhti, Hong Wang

Research output: Chapter in Book/Conference proceeding › Conference contribution › peer-review

Abstract

Differential Evolution (DE) is a simple evolutionary algorithm which is inherently adaptive. This is due to the fact that the mutation amount is derived from the difference of randomly chosen members of the population, which is automatically reduced as the population diversity drops. The process is however governed by an important weighing parameter F, to which the global properties of the DE are very sensitive. Large F can lead to significant reductions in convergence speed, whilst small F can cause the algorithm to get stuck. In this paper, a simple co-evolutionary process is proposed to automatically update the F parameter during the optimization process based on a uniformly distributed update rule. The behavior of the adaptive DE is studied and investigated with some benchmark functions. © 2006 IEEE.

Original language	English
Title of host publication	IEEE International Symposium on Intelligent Control - Proceedings\|IEEE Int. Symp. Intell. Control Proc.
Pages	1264-1269
Number of pages	5
DOIs	https://doi.org/10.1109/ISIC.2006.285624
Publication status	Published - 2006
Event	Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC) - Munich Duration: 1 Jul 2006 → …

Conference

Conference	Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC)
City	Munich
Period	1/07/06 → …

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10.1109/ISIC.2006.285624

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@inproceedings{9bfd3dcebd46423da0b151997efb3340,

title = "Co-evolutionary self-adaptive differential evolution with a uniform-distribution update rule",

abstract = "Differential Evolution (DE) is a simple evolutionary algorithm which is inherently adaptive. This is due to the fact that the mutation amount is derived from the difference of randomly chosen members of the population, which is automatically reduced as the population diversity drops. The process is however governed by an important weighing parameter F, to which the global properties of the DE are very sensitive. Large F can lead to significant reductions in convergence speed, whilst small F can cause the algorithm to get stuck. In this paper, a simple co-evolutionary process is proposed to automatically update the F parameter during the optimization process based on a uniformly distributed update rule. The behavior of the adaptive DE is studied and investigated with some benchmark functions. {\textcopyright} 2006 IEEE.",

author = "Amin Nobakhti and Hong Wang",

year = "2006",

doi = "10.1109/ISIC.2006.285624",

language = "English",

isbn = "0780397983",

pages = "1264--1269",

booktitle = "IEEE International Symposium on Intelligent Control - Proceedings|IEEE Int. Symp. Intell. Control Proc.",

note = "Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC) ; Conference date: 01-07-2006",

}

Nobakhti, A & Wang, H 2006, Co-evolutionary self-adaptive differential evolution with a uniform-distribution update rule. in IEEE International Symposium on Intelligent Control - Proceedings|IEEE Int. Symp. Intell. Control Proc.. pp. 1264-1269, Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC), Munich, 1/07/06. https://doi.org/10.1109/ISIC.2006.285624

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AU - Wang, Hong

PY - 2006

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N2 - Differential Evolution (DE) is a simple evolutionary algorithm which is inherently adaptive. This is due to the fact that the mutation amount is derived from the difference of randomly chosen members of the population, which is automatically reduced as the population diversity drops. The process is however governed by an important weighing parameter F, to which the global properties of the DE are very sensitive. Large F can lead to significant reductions in convergence speed, whilst small F can cause the algorithm to get stuck. In this paper, a simple co-evolutionary process is proposed to automatically update the F parameter during the optimization process based on a uniformly distributed update rule. The behavior of the adaptive DE is studied and investigated with some benchmark functions. © 2006 IEEE.

AB - Differential Evolution (DE) is a simple evolutionary algorithm which is inherently adaptive. This is due to the fact that the mutation amount is derived from the difference of randomly chosen members of the population, which is automatically reduced as the population diversity drops. The process is however governed by an important weighing parameter F, to which the global properties of the DE are very sensitive. Large F can lead to significant reductions in convergence speed, whilst small F can cause the algorithm to get stuck. In this paper, a simple co-evolutionary process is proposed to automatically update the F parameter during the optimization process based on a uniformly distributed update rule. The behavior of the adaptive DE is studied and investigated with some benchmark functions. © 2006 IEEE.

U2 - 10.1109/ISIC.2006.285624

DO - 10.1109/ISIC.2006.285624

M3 - Conference contribution

SN - 0780397983

SN - 9780780397989

SP - 1264

EP - 1269

BT - IEEE International Symposium on Intelligent Control - Proceedings|IEEE Int. Symp. Intell. Control Proc.

T2 - Joint 2006 IEEE Conference on Control Applications (CCA), Computer-Aided Control Systems Design Symposium (CACSD) and International Symposium on Intelligent Control (ISIC)

Y2 - 1 July 2006

ER -

Co-evolutionary self-adaptive differential evolution with a uniform-distribution update rule

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