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

TL;DR

This paper discusses enhancements to the R GA package, including hybrid genetic algorithms with local search, parallel computing capabilities, and islands evolution, improving efficiency and solution quality.

Contribution

Introduces new features in the GA package such as hybrid GAs, parallelisation methods, and islands evolution, enhancing optimization performance and flexibility.

Findings

Hybrid GAs improve solution quality and speed.

Parallelisation methods increase computational efficiency.

Real-world and benchmark tests demonstrate effectiveness.

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.