  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.
