Survival of the fittest - genetic algorithms versus evolution strategies in the optimization of systems models

Mayer, DG, Belward, JA, Widell, H and Burrage, K (1999) Survival of the fittest - genetic algorithms versus evolution strategies in the optimization of systems models. Agricultural Systems, 60 2: 113-122. doi:10.1016/S0308-521X(99)00022-0


Author Mayer, DG
Belward, JA
Widell, H
Burrage, K
Title Survival of the fittest - genetic algorithms versus evolution strategies in the optimization of systems models
Formatted title
Survival of the fittest—genetic algorithms versus evolution strategies in the optimization of systems models
Journal name Agricultural Systems   Check publisher's open access policy
ISSN 0308-521X
1873-2267
Publication date 1999-05
Sub-type Article (original research)
DOI 10.1016/S0308-521X(99)00022-0
Volume 60
Issue 2
Start page 113
End page 122
Total pages 10
Place of publication U.K
Publisher Elsevier Science
Collection year 1999
Language eng
Subject C1
780101 Mathematical sciences
230116 Numerical Analysis
010301 Numerical Analysis
Abstract The use of numerical optimization techniques on simulation models is a developing field. Many of the available algorithms are not well suited to the types of problems posed by models of agricultural systems. Coming from different historical and developmental backgrounds, both genetic algorithms and evolution strategies have proven to be thorough and efficient methods in identifying the global optimum of such systems. A challenging herd dynamics model is used to test and compare optimizations using binary and real-value genetic algorithms, as well as evolution strategies. All proved successful in identifying the global optimum of this model, but evolution strategies were notably slower in achieving this. As the more successful innovations of each of these methods are being commonly adopted by all, the boundaries between them are becoming less clear-cut. They are effectively merging into one general class of optimization methods now termed evolutionary algorithms. (C) 1999 Elsevier Science Ltd. All rights reserved.
Keyword Agriculture, Multidisciplinary
Optimization
Model
Genetic Algorithm
Evolution Strategy
Rainfall-runoff Models
Agricultural Systems
Simulation
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Q-Index Code C1

Document type: Journal Article
Sub-type: Article (original research)
Collection: Institute for Molecular Bioscience - Publications
 
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Citation counts: TR Web of Science Citation Count  Cited 20 times in Thomson Reuters Web of Science Article | Citations
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Created: Tue, 10 Jun 2008, 13:31:29 EST