The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
GA: A Package for Genetic Algorithms in R
Open accessAbstract
Genetic algorithms (GAs) are stochastic search algorithms inspired by the basic principles of biological evolution and natural selection. GAs simulate the evolution of living organisms, where the fittest individuals dominate over the weaker ones, by mimicking the biological mechanisms of evolution, such as selection, crossover and mutation. GAs have been successfully applied to solve optimization problems, both for continuous (whether differentiable or not) and discrete functions. This paper describes the R package GA, a collection of general purpose functions that provide a flexible set of tools for applying a wide range of genetic algorithm methods. Several examples are discussed, ranging from mathematical functions in one and two dimensions known to be hard to optimize with standard derivative-based methods, to some selected statistical problems which require the optimization of user defined objective functions. (This paper contains animations that can be viewed using the Adobe Acrobat PDF viewer.)
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Cites
R: A Language and Environment for Statistical Computing
R Core Team (2025) · doi:10.32614/r.manuals · Cited by 352,181
Holland, J. H. (1992) · The MIT Press eBooks · doi:10.7551/mitpress/1090.001.0001 · Cited by 47,002
Genetic Algorithms in Search, Optimization and Machine Learning
Goldberg, D. E., & Robson, D. (1988) · Cited by 18,391
Information Theory and an Extension of the Maximum Likelihood Principle
Akaike, H. (1998) · Springer Series in Statistics · doi:10.1007/978-1-4612-1694-0_15 · Cited by 17,781
Goldberg, D. E., & Shakespeare, W. (2002) · Cited by 16,088
No free lunch theorems for optimization
Wolpert, D. H., & Macready, W. G. (1997) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.585893 · Cited by 14,237
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Introduction to Evolutionary Computing
Eiben, A. E., & Smith, J. E. (2003) · Natural Computing Series · doi:10.1007/978-3-662-05094-1 · Cited by 4,176
Introduction to Stochastic Search and Optimization: Estimation, Simulation, and Control
Spall, J. C. (2003) · Wiley eBooks · doi:10.1002/0471722138 · Cited by 1,890
Cited by
ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms
Peterman, W. E. (2018) · Methods in Ecology and Evolution · doi:10.1111/2041-210x.12984 · Cited by 280
Preliminary predictive criteria for COVID-19 cytokine storm
Caricchio, R., Gallucci, M., Dass, C., Zhang, X., Gallucci, S., Fleece, D., Bromberg, M., & Criner, G. J. (2021) · Annals of the Rheumatic Diseases · doi:10.1136/annrheumdis-2020-218323 · Cited by 230
High-throughput 5’ UTR engineering for enhanced protein production in non-viral gene therapies
Cao, J., Novoa, E. M., Zhang, Z., Chen, W. C. W., Liu, D., Choi, G. C. G., Wong, A. S. L., Wehrspaun, C., Kellis, M., & Lu, T. K. (2021) · Nature Communications · doi:10.1038/s41467-021-24436-7 · Cited by 159
Sahdra, B. K., Ciarrochi, J., Parker, P., & Scrucca, L. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.00189 · Cited by 100
Self-citationBody Image–Acceptance and Action Questionnaire–5: An Abbreviation Using Genetic Algorithms
Basarkod, G., Sahdra, B., & Ciarrochi, J. (2018) · Behavior Therapy · doi:10.1016/j.beth.2017.09.006 · Cited by 77
“The computational details of the GA (Holland, 1975; Scrucca, 2013), and its specific application to questionnaire abbreviation are discussed elsewhere (Sahdra et al.”
Background
Metadata
- Authors
- Scrucca, L.
- Venue
- Journal of Statistical Software
- Publisher
- Foundation for Open Access Statistic
- Published
- 2013
- Volume
- 53
- Issue
- 4
- ISSN
- 1548-7660 (ISSN-L)
- OpenAlex
- W2171149164
- Cited by
- 837
- References
- 27
- FWCI
- 33.87
- Type
- journal article
- Language
- en
- Open access
- diamond
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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