Abstract
Maintaining population diversity is a challenge for the success of genetic algorithm. A numerous approaches have been proposed by researchers for adding diversity to the population. Dual-population genetic algorithm (DPGA) is one of them which is an effective optimization algorithm and provides diversity to the main population. Problems in GA such as premature convergence and population diversity is well addressed by DPGA. The aim of writing this review paper is to study how DPGA has been evolved. DPGA is inherently parallelizable, and hence, it can be port to parallel programming architecture for large-scale or large-dimension problems.
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