Genetic Algorithm: A Study of Parallel Genetic Algorithms
This study attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms, including a categorization of the techniques used to parallelize GAs.
Abstract
Genetic algorithms (GAs) are powerful search techniques that are used successfully to solve problems in many different disciplines. Parallel GAs are particularly easy to implement and promise substantial gains in performance. Genetic Algorithms (GAs) are powerful search techniques that are used to solve difficult problems in many disciplines. This study attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the literature, the paper presents a categorization of the techniques used to parallelize GAs, and shows examples of all of them. However, since the majority of the research in this field has concentrated on parallel GAs with multiple populations, the study focuses on this type of algorithms. Also, the paper describes some of the most significant problems in modeling and designing multi-population parallel GAs and presents some recent advancement.