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

Autor:	Luca Scrucca
Rok vydání:	2016
Předmět:	Parallel computing FOS: Computer and information sciences Stochastic optimisation Hybrid search Genetic algorithms Statistics - Computation Genetic algorithms R programming environment Stochastic optimisation Hybrid search Parallel computing Computation (stat.CO) R programming environment
Zdroj:	Luca Scrucca Publons
DOI:	10.48550/arxiv.1605.01931
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ceef8a0abfeed9c124b80b3a07926557 Zobrazit plný text záznamu