RT Journal Article
T1 Parallel genetic algorithms: a useful survey
A1 Harada, Tomohiro
A1 Alba-Torres, Enrique
K1 Algoritmos genéticos
K1 Optimización combinatoria
AB In this article, we encompass an analysis of the recent advances in parallel genetic algorithms (PGAs). We have selected these algorithms because of the deep interest in many research fields for techniques that can face complex applications where running times and other computational resources are greedily consumed by present solvers, and PGAs act then as efficient procedures that fully use modern computational platforms at the same time that allow the resolution of cutting edge open problems. We have faced this survey on PGAs with the aim of helping newcomers or busy researchers who want to have a wide vision on the field. Then, we discuss the most well-known models and their implementations from a recent (last six years) and useful point of view: we discuss on highly cited articles, keywords, the venues where they can be found, a very comprehensive (and new) taxonomy covering different research domains involved in PGAs, and a set of recent applications. We also introduce a new vision on open challenges, and try to give hints that guide practitioners and specialized researchers. Our conclusion is that there are many advantages to using these techniques, and lots of potential interactions to other evolutionary algorithms, as well as we contribute to creating a body of knowledge in PGAs by summarizing them in a structured way, so that the reader can find this article usefulfor practical research, graduate teaching, and as a pedagogical guide to this exciting domain.
PB Association for Computing Machinery (ACM)
YR 2020
FD 2020-08-20
LK https://hdl.handle.net/10630/36848
UL https://hdl.handle.net/10630/36848
LA eng
NO Tomohiro Harada and Enrique Alba. 2020. Parallel Genetic Algorithms: A Useful Survey. ACM Comput. Surv. 53, 4, Article 86 (August 2020), 39 pages. https://doi.org/10.1145/3400031
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 21 ene 2026