Untitled

Abstract

Genetic Algorithm (GA), a stochastic optimization technique, doesn't ensure optimal solution every time. Nowadays there is a need to improve the performance of each and every application so that the time required for obtaining quality solution can be minimized. This paper gives a brief overview of theoretical advances and computing trends, particularly population diversity in PGA (Parallel GA) and provides information about how various authors, researchers, scientists have parallelized GA over various parallel computing paradigms viz. Cluster, MPP (Massively Parallel Processing), GPGPU (General purpose Graphics Processing Units), Grid, Cloud, Multicore/HPC to ensure more optimal solution every time with efficacy and efficiency.