Evolutionary algorithm (EA) is a generic population-based metaheuristic optimization algorithm. An EA uses home mechanisms inspired by biological evolution: reproduction, mutation, recombination, and selection. Candidate solutions to the optimization problem play the role of individuals in a population, and the tness function determines the environment within which the solutions "live" . Evolution of the population then takes place after the repeated application of the above operators.This description is deliberately based on a unifying view presenting a general scheme that forms the common basis of all Evolutionary Algorithm variants. The main components of EAs are discussed, explaining their role and related issues of terminology. Further on we discuss general issues for EAs concerning their working. Finally, we put EAs into a broader context and explain their relation with other global optimisation techniques.
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