DAFHEA: A Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm for optimisation problems

Maumita Bhattacharya; Guojun Lu

doi:10.1109/CEC.2003.1299903

DAFHEA: A Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm for optimisation problems

Maumita Bhattacharya, Guojun Lu

Research output: Book chapter/Published conference paper › Conference paper › peer-review

18 Citations (Scopus)

8 Downloads (Pure)

Abstract

A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluations speeds up the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.

Original language	English
Title of host publication	IEEE Congress on Evolutionary Computation 2003 (CEC 2003)
Place of Publication	USA
Publisher	IEEE Press
Pages	1879-1886
Number of pages	8
Volume	3
ISBN (Electronic)	0780378059
DOIs	https://doi.org/10.1109/CEC.2003.1299903
Publication status	Published - 2003
Event	IEEE Congress on Evolutionary Computation - Canberra, Australia, Australia Duration: 08 Dec 2003 → 12 Dec 2003

Conference

Conference	IEEE Congress on Evolutionary Computation
Country/Territory	Australia
Period	08/12/03 → 12/12/03

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10.1109/CEC.2003.1299903

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title = "DAFHEA: A Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm for optimisation problems",

abstract = "A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluations speeds up the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.",

keywords = "Open access version available, Approximate models, Clustering, Evolutionary Computation",

author = "Maumita Bhattacharya and Guojun Lu",

note = "Imported on 03 May 2017 - DigiTool details were: publisher = USA: IEEE Press, 2003. Event dates (773o) = 8-12 December 2003; Parent title (773t) = IEEE Congress on Evolutionary Computation.; IEEE Congress on Evolutionary Computation ; Conference date: 08-12-2003 Through 12-12-2003",

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language = "English",

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AU - Bhattacharya, Maumita

AU - Lu, Guojun

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PY - 2003

Y1 - 2003

N2 - A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluations speeds up the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.

AB - A dynamic approximate fitness-based hybrid evolutionary algorithm is presented here. The proposed model partially replaces expensive fitness evaluation by an approximate model. A cluster-based intelligent guided technique is used to decide on use of expensive function evaluation and dynamically adapt the predicted model. Avoiding expensive function evaluations speeds up the optimisation process. Also additional information derived from the predicted model at lower computational expense, is exploited to improve solution. Experimental findings support the theoretical basis of the proposed framework.

KW - Open access version available

KW - Approximate models

KW - Clustering

KW - Evolutionary Computation

U2 - 10.1109/CEC.2003.1299903

DO - 10.1109/CEC.2003.1299903

M3 - Conference paper

VL - 3

SP - 1879

EP - 1886

BT - IEEE Congress on Evolutionary Computation 2003 (CEC 2003)

PB - IEEE Press

CY - USA

Y2 - 8 December 2003 through 12 December 2003

ER -

DAFHEA: A Dynamic Approximate Fitness based Hybrid Evolutionary Algorithm for optimisation problems

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