Genetic algorithms are stochastic iterative algorithms in which a population of individuals evolve by emulating the process of biological evolution and natural selection. The R package GA provides a collection of general purpose functions for optimisation using genetic algorithms. This paper describes some enhancements recently introduced in version 3 of the package. In particular, hybrid GAs have been implemented by including the option to perform local searches during the evolution. This allows to combine the power of genetic algorithms with the speed of a local optimiser. Another major improvement is the provision of facilities for parallel computing. Parallelisation has been implemented using both the master-slave approach and the islands evolution model. Several examples of usage are presented, with both real-world data examples and benchmark functions, showing that often high-quality solutions can be obtained more efficiently.
rgenoud, Rmalschains, DEoptim, GenSA, pso, cmaes, tabuSearch, GA, quantmod, doParallel, foreach, iterators, doRNG, forecast, astsa, globalOptTests, Rcpp, memoise
Optimization, HighPerformanceComputing, Finance, MachineLearning, TimeSeries, Econometrics, Environmetrics, NumericalMathematics
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For attribution, please cite this work as
Scrucca, "On Some Extensions to GA Package: Hybrid Optimisation, Parallelisation and Islands EvolutionOn some extensions to GA package: hybrid optimisation, parallelisation and islands evolution", The R Journal, 2017
BibTeX citation
@article{RJ-2017-008,
author = {Scrucca, Luca},
title = {On Some Extensions to GA Package: Hybrid Optimisation, Parallelisation and Islands EvolutionOn some extensions to GA package: hybrid optimisation, parallelisation and islands evolution},
journal = {The R Journal},
year = {2017},
note = {https://doi.org/10.32614/RJ-2017-008},
doi = {10.32614/RJ-2017-008},
volume = {9},
issue = {1},
issn = {2073-4859},
pages = {187-206}
}