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Genetic Algorithms and Machine Learning

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Goldberg, David E., Holland, John H.·1988·Machine Learning

DOI 10.1023/a:1022602019183

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Genetic algorithms in search, optimization, and machine learning
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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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Authors: Goldberg, D. E., & Holland, J. H.
Affiliations: University of Alabama (US)
University of Michigan (US)
Venue: Machine Learning
Publisher: Springer Science and Business Media LLC
Published: 1988-10
Volume: 3
Issue: 2-3
Pages: 95-99
ISSN: 0885-6125 (ISSN-L)
1573-0565
DOI: 10.1023/a:1022602019183
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: www.springernature.com/gp/researchers/text-and-data-mining (tdm)
www.springernature.com/gp/researchers/text-and-data-mining (vor)
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

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