A survey of parallel genetic algorithms

ABSTRACT. Genetic algorithms (GAs) are powerful search techniques that are used success-fully to solve problems in many different disciplines. Parallel GAs are particularly easy to im-plement and promise substantial gains in performance. As such, there has been extensive re-search in this field. This survey attempts to collect, organize, and present in a unified way some of the most representative publications on parallel genetic algorithms. To organize the litera-ture, 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 concen-trated on parallel GAs with multiple populations, the survey 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 advancements