Paper Info
Title
Parallel Genetic Algorithms: A Useful Survey
Abstract
AbstractIn 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, we contribute to creating a body of knowledge in PGAs by summarizing them in a structured way, so the reader can find this article useful for practical research, graduate teaching, and as a pedagogical guide to this exciting domain.
Year
DOI
Venue
2020
10.1145/3400031
ACM Computing Surveys
Keywords
DocType
Volume
Parallelism, genetic algorithms, complex problem solving, time consuming applications, new areas for search, optimization, and learning, last five years, artificial intelligence
Journal
53
Issue
ISSN
Citations 
4
0360-0300
2
PageRank 
References 
Authors
0.38
0
2
Name
Order
Citations
PageRank
Tomohiro Harada14422.99
Alba Enrique21438.74