Evolutionary algorithms performance evaluation using rank-based multiple comparison procedure

Research output: Contribution to Book/Report typesContribution to conference proceedingspeer-review

Abstract

Comparing the performance of different evolutionary optimization algorithms is not trivial and is an important task to understand and develop new and more effective approaches. Also, as EAs are stochastic process with unknown distribution, performance comparison must be done with the proper statistical tools and procedures. This paper proposes a generic comparison procedure to evaluate simultaneously the performance of several different EAs. The proposed comparison procedure has the advantage to be statistically consistent and adequate to evaluate performance analysis of the EAs. With the ranking scheme, it also allows a unification of the three major criteria under a unique ranking number without the use of using arbitrary artefacts such as weighting coefficients or function objective transformations.

Original languageEnglish
Title of host publicationWMSCI 2007 - The 11th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 13th International Conference on Information Systems Analysis and Synthesis, ISAS 2007 - Proc.
Pages35-40
Number of pages6
Publication statusPublished - 2007
Event11th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2007, Jointly with the 13th International Conference on Information Systems Analysis and Synthesis, ISAS 2007 - Orlando, FL, United States
Duration: 8 Jul 200711 Jul 2007

Publication series

NameWMSCI 2007 - The 11th World Multi-Conference on Systemics, Cybernetics and Informatics, Jointly with the 13th International Conference on Information Systems Analysis and Synthesis, ISAS 2007 - Proc.
Volume1

Conference

Conference11th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2007, Jointly with the 13th International Conference on Information Systems Analysis and Synthesis, ISAS 2007
Country/TerritoryUnited States
CityOrlando, FL
Period8/07/0711/07/07

!!!Keywords

  • Evolutionary algorithm
  • Hypothesis testing
  • Multiple comparison
  • Optimization
  • Performance comparison

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