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Evolutionary algorithm (EA) emerges as an important optimization and search technique in the last decade. EA is a subset of Evolutionary Computations (EC) and belongs to set of modern heuristics based search method. Due to flexible nature and robust behavior inherited from Evolutionary Computation, it becomes efficient means of problem solving method for widely used global optimization problems. It can be used successfully in many applications of high complexity. This paper presents a critical overview of Evolutionary algorithms and its generic procedure for implementation. It further discusses the various practical advantages using evolutionary algorithms over classical methods of optimization. It also includes unusual study of various invariants of EA like Genetic Programming (GP), Genetic Algorithm (GA), Evolutionary Programming (EP) and Evolution Strategies (ES). Extensions of EAs in the form of Memetic algorithms (MA) and distributed EA are also discussed. Further the paper focuses on various refinements done in area of EA to solve real life problems.
Pradnya Vikhar (Thu,) studied this question.
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