The DOI matches its Crossref record; 13 of the 13 references it cites were found in a registry or an index.
Fitness uniform optimization
Open accessAbstract
In evolutionary algorithms, the fitness of a population increases with time by mutating and recombining individuals and by a biased selection of fitter individuals. The right selection pressure is critical in ensuring sufficient optimization progress on the one hand and in preserving genetic diversity to be able to escape from local optima on the other hand. Motivated by a universal similarity relation on the individuals, we propose a new selection scheme, which is uniform in the fitness values. It generates selection pressure toward sparsely populated fitness regions, not necessarily toward higher fitness, as is the case for all other selection schemes. We show analytically on a simple example that the new selection scheme can be much more effective than standard selection schemes. We also propose a new deletion scheme which achieves a similar result via deletion and show how such a scheme preserves genetic diversity more effectively than standard approaches. We compare the performance of the new schemes to tournament selection and random deletion on an artificial deceptive problem and a range of NP hard problems: traveling salesman, set covering, and satisfiability
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“This approach, which aims to preserve diversity55,56, is related to Lexicase57 in that both approaches consider a set of test cases for scoring an individual, and it is related to fitness uniform optimization58, which also clusters individuals on the basis of their fitness value; however, we sample the clusters on the basis of their score instead of uniformly, as detailed next.”
From Mathematical discoveries from program search with large language models (2024) · cites this paper as [58]
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Cites
An Effective Heuristic Algorithm for the Traveling-Salesman Problem
Lin, S., & Kernighan, B. W. (1973) · Operations Research · doi:10.1287/opre.21.2.498 · Cited by 3,851
A Comparative Analysis of Selection Schemes Used in Genetic Algorithms
Goldberg, D. E., & Deb, K. (1991) · Foundations of genetic algorithms · doi:10.1016/b978-0-08-050684-5.50008-2 · Cited by 2,456
Parameter control in evolutionary algorithms
Eiben, A. E., Hinterding, R., & Michalewicz, Z. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.771166 · Cited by 1,987
An Analysis Of The Behavior Of A Class Of Genetic Adaptive Systems.
DeJong, K. A. (1975) · Deep Blue (University of Michigan) · doi:10.7302/10966 · Cited by 1,299
Eshelman, L. J. (1991) · Foundations of genetic algorithms · doi:10.1016/b978-0-08-050684-5.50020-3 · Cited by 1,042
A genetic algorithm for the set covering problem
Beasley, J. E., & Chu, P. C. (1996) · European Journal of Operational Research · doi:10.1016/0377-2217(95)00159-x · Cited by 638
A Comparison of Selection Schemes Used in Evolutionary Algorithms
Blickle, T., & Thiele, L. (1996) · Evolutionary Computation · doi:10.1162/evco.1996.4.4.361 · Cited by 601
The Science of Breeding and Its Application to the Breeder Genetic Algorithm (BGA)
Mühlenbein, H., & Schlierkamp-Voosen, D. (1993) · Evolutionary Computation · doi:10.1162/evco.1993.1.4.335 · Cited by 245
Combining simulated annealing with local search heuristics
Martin, O., & Otto, S. W. (1996) · Annals of Operations Research · doi:10.1007/bf02601639 · Cited by 219
Strategy adaptation by competing subpopulations
Schlierkamp-Voosen, D., & Mühlenbein, H. (1994) · Lecture notes in computer science · doi:10.1007/3-540-58484-6_264 · Cited by 142
Genetic drift in genetic algorithm selection schemes
Rogers, A., & Prügel‐Bennett, A. (1999) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.797972 · Cited by 128
What Makes a Problem Hard for a Genetic Algorithm? Some Anomalous Results and Their Explanation
Forrest, S., & Mitchell, M. (1993) · Machine Learning · doi:10.1023/a:1022626114466 · Cited by 114
de la Maza, M., & Tidor, B. (1993) · international conference on Genetic algorithms · Cited by 71
Research works printed but not linked: 22
- Goldberg, 1989, Genetic Algorithms in Search, Optimization, and Machine Learning
- Blickle, A mathematical analysis of tournament selection, 9
- Holland, 1975, Adpatation in Natural and Artificial Systems
- Whitley, The GENITOR algorithm and selection pressure: why rank-based allocation of reproductive trials is best, 116
- Baker, Adaptive selection methods for genetic algorithms, 101
- Bäck, A survey of evolution strategies, 2
- Herdy, 1992, Reproductive isolation as strategy parameter in hierarchically organized evolution strategies, 207
- Goldberg, Genetic algorithms with sharing for multimodal function optimization, 41
- Collins, Selection in massively parallel genetic algorithms, 249
- Rudolph, On takeover times in spatially structured populations: array and ring, 144
- Cavicchio, 1970, Adaptive search using simulated evolution
- Hutter, Fitness uniform selection to preserve genetic diversity, 783
- Legg, Tournament versus fitness uniform selection, 2144
- Legg, Fitness uniform deletion for robust optimization, 1271
- Hutter, 1991, Implementierung eines Klassifizierungs-Systems
- Rogers, 1999, Modeling the dynamics of a steady-state genetic algorithm, 57
- Johnson, 1997, The traveling salesman problem: a case study, 215
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Cited by
Mathematical discoveries from program search with large language models
Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., et al. (2024) · Nature · doi:10.1038/s41586-023-06924-6 · Cited by 544
“…to Lexicase 57 in that both approaches consider a set of test cases for scoring an individual, and it is related to fitness uniform optimization 58 , which also clusters individuals on the basis of their fitness value; however, we sample the clusters on the basis of their score instead of…”
Background
Metadata
- Authors
- Hutter, M., & Legg, S.
- Affiliations
- Dalle Molle Institute for Artificial Intelligence Research (CH)
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2006-10
- Volume
- 10
- Issue
- 5
- Pages
- 568-589
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2117336417
- Cited by
- 63
- References
- 13
- Citations by year
- 2026: 1 · 2025: 3 · 2024: 3 · 2023: 4 · 2022: 3 · 2020: 4 · 2019: 3 · 2018: 2 · 2017: 2 · 2016: 7 · 2015: 2 · 2014: 4 · 2013: 4 · 2012: 3
- FWCI
- 4.19
- Type
- journal article
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE Transactions on Evolutionary Computation (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- ANU Open Research (Australian National University) (submittedVersion)
- arXiv (Cornell University)
- Funders
- Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung (107616, 67712, 200020)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Evolution and Genetic Dynamics
- Keywords
- selection pressure, genetic diversity, tournament selection, random deletion, deceptive problems, NP-hard combinatorial optimization, traveling salesman problem, set covering problem, satisfiability, local optima avoidance
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