Genetic algorithms (GA) is an optimization search algorithm which follows the theory of "survival of the fittest" formulated by Darwin. Genetic algorithm mimics the process of natural selection where to produce every subsequent generation the individuals that have the highest fitness value among the current population are selected. This paper focuses on the selection stage and provides a comparative analysis of the different selection techniques that have been used in GA. This review also contains a brief coverage of the various study fields related to genetic algorithm along with future research directions. The most interesting genetic algorithms among the research community and their selection approaches have been selected for investigation. New as well as sophisticated researchers dealing with NP-hard problems where selection strategy plays crucial role are provided with an accurate comparison of selection techniques in light of GA`s state-of-the-art applications. The implementation of well-known algorithms is shown, along with the benefits and drawbacks of each.

Genetic Algorithm, Selection Technique, Tournament Selection, Ranked Based Selection, Truncation Selection, Optimal Solution, Roulette Wheel Selection

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