research-article

Application domain study of evolutionary algorithms in optimization problems

Authors:

R. J. DuroAuthors Info & Claims

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

Pages 495 - 502

https://doi.org/10.1145/1389095.1389190

Published: 12 July 2008 Publication History

Abstract

This paper deals with the problem of comparing and testing evolutionary algorithms, that is, the benchmarking problem, from an analysis point of view. A practical study of the application domain of four representative evolutionary algorithms is carried out using a relevant set of real-parameter function optimization benchmarks. The four selected algorithms are the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and the Differential Evolution (DE), due to their successful results in recent studies, a Genetic Algorithm with real parameter operators, used here as a reference approach because it is probably the most familiar to researchers, and the Macroevolutionary algorithm (MA), which is not widely known but it shows a very remarkable behavior in some problems. The algorithms have been compared running several tests over the benchmark function set to analyze their capabilities from a practical point of view, in other words, in terms of their usability. The characterization of the algorithms is based on accuracy, stability and time consumption parameters thus establishing their operational scope and the type of optimization problems they are more suitable for.

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Cited By

Sengupta RPal MSaha SBandyopadhyay S(2020)Uniform distribution driven adaptive differential evolutionApplied Intelligence10.1007/s10489-020-01707-2Online publication date: 22-Jun-2020
https://doi.org/10.1007/s10489-020-01707-2
CaamañO PBellas FBecerra JDuro R(2013)Evolutionary algorithm characterization in real parameter optimization problemsApplied Soft Computing10.1016/j.asoc.2013.01.00213:4(1902-1921)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.asoc.2013.01.002
Tedín RBecerra JDuro R(2013)Building the “Automatic Body Condition Assessment System” (ABiCA), an Automatic Body Condition Scoring System using Active Shape Models and Machine LearningRecent Advances in Knowledge-based Paradigms and Applications10.1007/978-3-319-01649-8_10(145-168)Online publication date: 31-Oct-2013
https://doi.org/10.1007/978-3-319-01649-8_10
Show More Cited By

Index Terms

Application domain study of evolutionary algorithms in optimization problems
1. Computing methodologies
  1. Artificial intelligence
    1. Control methods
    2. Search methodologies
2. Theory of computation
  1. Design and analysis of algorithms
    1. Algorithm design techniques
      1. Dynamic programming

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cover image ACM Conferences

GECCO '08: Proceedings of the 10th annual conference on Genetic and evolutionary computation

July 2008

1814 pages

ISBN:9781605581309

DOI:10.1145/1389095

Conference Chair:
Conor Ryan
University of Limerick, Ireland
,
Editor:
Maarten Keijzer
Chordiant Software International B.V., The Netherlands

Copyright © 2008 ACM.

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Published: 12 July 2008

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July 12 - 16, 2008

GA, Atlanta, USA

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Overall Acceptance Rate 1,669 of 4,410 submissions, 38%

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Cited By

Sengupta RPal MSaha SBandyopadhyay S(2020)Uniform distribution driven adaptive differential evolutionApplied Intelligence10.1007/s10489-020-01707-2Online publication date: 22-Jun-2020
https://doi.org/10.1007/s10489-020-01707-2
CaamañO PBellas FBecerra JDuro R(2013)Evolutionary algorithm characterization in real parameter optimization problemsApplied Soft Computing10.1016/j.asoc.2013.01.00213:4(1902-1921)Online publication date: 1-Apr-2013
https://dl.acm.org/doi/10.1016/j.asoc.2013.01.002
Tedín RBecerra JDuro R(2013)Building the “Automatic Body Condition Assessment System” (ABiCA), an Automatic Body Condition Scoring System using Active Shape Models and Machine LearningRecent Advances in Knowledge-based Paradigms and Applications10.1007/978-3-319-01649-8_10(145-168)Online publication date: 31-Oct-2013
https://doi.org/10.1007/978-3-319-01649-8_10
Caamaño PBecerra JBellas FDuro RLanzi P(2011)Are evolutionary algorithm competitions characterizing landscapes appropriatelyProceedings of the 13th annual conference companion on Genetic and evolutionary computation10.1145/2001858.2002071(695-702)Online publication date: 12-Jul-2011
https://dl.acm.org/doi/10.1145/2001858.2002071

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