The R Journal: accepted article

This article will be copy edited and may be changed before publication.

On some extensions to GA package: hybrid optimisation, parallelisation and islands evolution
Luca Scrucca

Abstract 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.

Received: 2016-05-29; online 2017-05-10
CRAN packages: rgenoud, Rmalschains, DEoptim, GenSA, pso, cmaes, tabuSearch, GA, quantmod, doParallel, foreach, iterators, doRNG, forecast, astsa, globalOptTests, Rcpp, memoise , CRAN Task Views cited directly: Optimization, HighPerformanceComputing , CRAN Task Views implied by cited CRAN packages: Optimization, HighPerformanceComputing, Finance, MachineLearning, TimeSeries, Econometrics, Environmetrics, NumericalMathematics

CC BY 4.0
This article is licensed under a Creative Commons Attribution 4.0 International license.

  author = {Luca Scrucca},
  title = {{On some extensions to GA package: hybrid optimisation,
          parallelisation and islands evolution}},
  year = {2017},
  journal = {{The R Journal}},
  url = {}