research-article

An enhanced statistical approach for evolutionary algorithm comparison

Authors:

Eduardo G. Carrano,

Ricardo H.C. Takahashi,

Elizabeth F. WannerAuthors Info & Claims

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

Pages 897 - 904

https://doi.org/10.1145/1389095.1389270

Published: 12 July 2008 Publication History

Abstract

This paper presents an enhanced approach for comparing evolutionary algorithm. This approach is based on three statistical techniques: (a) Principal Component Analysis, which is used to make the data uncorrelated; (b) Bootstrapping, which is employed to build the probability distribution function of the merit functions; and (c) Stochastic Dominance Analysis, that is employed to make possible the comparison between two or more probability distribution functions. Since the approach proposed here is not based on parametric properties, it can be applied to compare any kind of quantity, regardless the probability distribution function. The results achieved by the proposed approach have provided more supported decisions than former approaches, when applied to the same problems.

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Cited By

Sarkar DBiswas A(2025)Complex preference analysis: a score-based evaluation strategy for ranking and comparison of the evolutionary algorithmsSoft Computing10.1007/s00500-025-10525-yOnline publication date: 3-Mar-2025
https://doi.org/10.1007/s00500-025-10525-y
Biswas ABiswas B(2017)Regression line shifting mechanism for analyzing evolutionary optimization algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2355-z21:21(6237-6252)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s00500-016-2355-z
Biswas ABiswas B(2014)Visual Analysis of Evolutionary Optimization AlgorithmsProceedings of the 2014 2nd International Symposium on Computational and Business Intelligence10.1109/ISCBI.2014.24(81-84)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.1109/ISCBI.2014.24
Show More Cited By

Index Terms

An enhanced statistical approach for evolutionary algorithm comparison
1. Mathematics of computing
  1. Mathematical analysis
    1. Mathematical optimization
2. Theory of computation
  1. Design and analysis of algorithms
    1. Mathematical optimization

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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.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 12 July 2008

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GECCO08

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GECCO08: Genetic and Evolutionary Computation Conference

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

Sarkar DBiswas A(2025)Complex preference analysis: a score-based evaluation strategy for ranking and comparison of the evolutionary algorithmsSoft Computing10.1007/s00500-025-10525-yOnline publication date: 3-Mar-2025
https://doi.org/10.1007/s00500-025-10525-y
Biswas ABiswas B(2017)Regression line shifting mechanism for analyzing evolutionary optimization algorithmsSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-016-2355-z21:21(6237-6252)Online publication date: 1-Nov-2017
https://dl.acm.org/doi/10.1007/s00500-016-2355-z
Biswas ABiswas B(2014)Visual Analysis of Evolutionary Optimization AlgorithmsProceedings of the 2014 2nd International Symposium on Computational and Business Intelligence10.1109/ISCBI.2014.24(81-84)Online publication date: 7-Dec-2014
https://dl.acm.org/doi/10.1109/ISCBI.2014.24
Carrano EMoreira LTakahashi R(2011)A new memory based variable-length encoding genetic algorithm for multiobjective optimizationProceedings of the 6th international conference on Evolutionary multi-criterion optimization10.5555/1987637.1987662(328-342)Online publication date: 5-Apr-2011
https://dl.acm.org/doi/10.5555/1987637.1987662
Carrano EWanner ETakahashi R(2011)A Multicriteria Statistical Based Comparison Methodology for Evaluating Evolutionary AlgorithmsIEEE Transactions on Evolutionary Computation10.1109/TEVC.2010.206956715:6(848-870)Online publication date: Dec-2011
https://doi.org/10.1109/TEVC.2010.2069567
Carrano EMoreira LTakahashi R(2011)A New Memory Based Variable-Length Encoding Genetic Algorithm for Multiobjective OptimizationEvolutionary Multi-Criterion Optimization10.1007/978-3-642-19893-9_23(328-342)Online publication date: 2011
https://doi.org/10.1007/978-3-642-19893-9_23

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