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Genetic Algorithms and Machine Learning
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“Gradient-free methods such as the Particle Swarm optimization168, Genetic Algorithms169 and Simulated Annealing170 algorithms are more demanding from a computational point of view”
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Genetic algorithms in search, optimization, and machine learning
(1989) · Choice Reviews Online · doi:10.5860/choice.27-0936 · Cited by 49,178
Adaptation in Natural and Artificial Systems
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,340
Self-citationGenetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Self-citationFoxman, D., & Bateson, G. (1973) · The Western Political Quarterly · doi:10.2307/446833 · Cited by 7,263
Bateson, G. (2000) · University of Chicago Press eBooks · doi:10.7208/chicago/9780226924601.001.0001 · Cited by 5,573
Seel, P. D. N. M. (2012) · Springer eBooks · doi:10.1007/978-1-4419-1428-6_5794 · Cited by 2,566
(1987) · Medical Entomology and Zoology · Cited by 2,512
Induction: Processes of Inference, Learning, and Discovery
Holland, J. H., Holyoak, K. J., Nisbett, R. E., Thagard, P. R., & Smoliar, S. W. (1987) · IEEE Expert · doi:10.1109/mex.1987.4307100 · Cited by 2,305
Self-citationHolland, J. H., Holyoak, K. J., Nisbett, R. E., & Thagard, P. R. (1986) · The MIT Press eBooks · doi:10.7551/mitpress/3729.001.0001 · Cited by 1,534
Self-citationOutline for a Logical Theory of Adaptive Systems
Holland, J. H. (1962) · Journal of the ACM · doi:10.1145/321127.321128 · Cited by 1,117
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- Waddington, C. H. (1967). Summary discussion. In P. S. Moorhead & M. M Kaplan (Eds.), Mathematical challenges to the neo-Darwinian interpretation of evolution. Philadelphia, PA: Wistar Institute Press.
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Metadata
- Authors
- Goldberg, D. E., & Holland, J. H.
- Affiliations
- University of Alabama (US)
- University of Michigan (US)
- Venue
- Machine Learning
- Published
- 1988-10
- Volume
- 3
- Issue
- 2-3
- Pages
- 95-99
- ISSN
- 0885-6125 (ISSN-L)
- 1573-0565
- OpenAlex
- W1541288193
- Cited by
- 3,193
- References
- 16
- Citations by year
- 2026: 105 · 2025: 151 · 2024: 160 · 2023: 204 · 2022: 249 · 2021: 342 · 2020: 378 · 2019: 366 · 2018: 265 · 2017: 239 · 2016: 212 · 2015: 145 · 2014: 162 · 2013: 94 · 2012: 29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Licence
- Locations
- Machine Learning (publishedVersion)
- Deep Blue (University of Michigan) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Evolutionary Algorithms and Applications
- Physical Sciences › Computer Science › Artificial Intelligence › Metaheuristic Optimization Algorithms Research
- Physical Sciences › Computer Science › Artificial Intelligence › Neural Networks and Applications
- Keywords
- genetic algorithm
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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