A Simple Statistical Test Against Origin-Biased Metaheuristics

Aidan Walden, Maxim Buzdalov*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference Proceeding (Non-Journal item)


One of the strong points of evolutionary algorithms and other similar metaheuristics is their robustness, which means that their performance is consistent across large varieties of problem settings. In particular, such algorithms avoid preferring one solution to another unless the optimized function gives enough reasons for doing that. This property is formally captured as invariance with regards to certain transformations of the search space and the problem definition, such as translation or rotation. The lack of some basic invariance properties in some recently proposed “nature-inspired” algorithms, together with the deliberate misuse of commonly used benchmark functions, can present them as excellent optimizers, which they are not. One particular class of such algorithms, origin-biased metaheuristics, are good at finding an optimum at the origin and are much worse for any other purpose. This paper presents a statistical testing procedure which can help to reveal such algorithms and to illustrate the negative aspects of their behavior. A case study involving 15 different algorithms shows that this test successfully detects most origin-biased algorithms.

Original languageEnglish
Title of host publicationApplications of Evolutionary Computation - 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Proceedings
EditorsStephen Smith, João Correia, Christian Cintrano
PublisherSpringer Nature
Number of pages16
ISBN (Print)9783031568510
Publication statusPublished - 21 Mar 2024
Event27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024 - Aberystwyth, United Kingdom of Great Britain and Northern Ireland
Duration: 03 Apr 202405 Apr 2024

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume14634 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024
Country/TerritoryUnited Kingdom of Great Britain and Northern Ireland
Period03 Apr 202405 Apr 2024


  • Biased algorithms
  • Nature-inspired algorithms
  • Statistical tests


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