GA: A Package for Genetic Algorithms in R

By Luca Scrucca. 2013

811 citations recorded by the source corpus.

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.)

Subfields: Artificial Intelligence, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/88f19058827b8cbc358f661dfa95cd3ae019532f

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Citations in the corpus

Assessing sources of uncertainty in length-based estimates of body growth in populations of fishes and macroinvertebrates with bootstrapped ELEFAN (2018, methodology): “, 2017) is a more complex VBGF curve fit based on a genetic algorithm (Scrucca, 2013).”
ResistanceGA: An R package for the optimization of resistance surfaces using genetic algorithms (2014, methodology): “The population continues to evolve until a sufficient number of generations have passed without an improvement in fitness (Scrucca, 2013).”
Who Is Impacted? Personality Predicts Individual Differences in Psychological Consequences of the COVID-19 Pandemic in Germany (2020, background): “1118 Social Psychological and Personality Science 12(6) fitness are more likely to mate and produce offspring models, repeated over several generations until fitness, evaluated by a selection criterion, does not improve any further (for details, see Bozdogan, 2004; Scrucca, 2013).”
Optimal Behavioral Hierarchy (2014, methodology): “We used a genetic algorithm to search the resulting space, as implemented by the GA library in R [47].”
Surrogate-Assisted Learning of Neural Networks (2017, methodology): “• GA [16] is a package which implements an genetic algorithm an allows optimization of real and integer problems.”