TY - GEN
T1 - A Simple Statistical Test Against Origin-Biased Metaheuristics
AU - Walden, Aidan
AU - Buzdalov, Maxim
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024/3/21
Y1 - 2024/3/21
N2 - 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.
AB - 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.
KW - Biased algorithms
KW - Nature-inspired algorithms
KW - Statistical tests
UR - http://www.scopus.com/inward/record.url?scp=85189642972&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-56852-7_21
DO - 10.1007/978-3-031-56852-7_21
M3 - Conference Proceeding (Non-Journal item)
AN - SCOPUS:85189642972
SN - 9783031568510
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 322
EP - 337
BT - Applications of Evolutionary Computation - 27th European Conference, EvoApplications 2024, Held as Part of EvoStar 2024, Proceedings
A2 - Smith, Stephen
A2 - Correia, João
A2 - Cintrano, Christian
PB - Springer Nature
T2 - 27th European Conference on Applications of Evolutionary Computation, EvoApplications 2024
Y2 - 3 April 2024 through 5 April 2024
ER -