Sanjay Jangid, Ravinder Puri and Thilak kumar
Evolutionary algorithm (EA) appears as asearch technique and important optimizationin the last period. EA is belonging to set of modern heuristics-based search method and it is a subgroup of Evolutionary Computations (EC). Due torobust behavior and flexible nature inherited from Evolutionary Computation.It becomes efficient means of problem-solvingway for broadly used global optimization problems. It can be used successfully in many applications of high complexity.This paper presents a critical overview of Evolutionary algorithms.It is generalmethod for implementation. It further discusses the various practical advantages using evolutionary algorithms over classical methods of optimization. It also comprises unusualstudy of various invariants of EA like Genetic Algorithm (GA), Genetic Programming (GP),Evolution Strategies (ES) and Evolutionary Programming (EP). Extensions of EAs in the form of Memetic algorithms (MA) and distributed EA are also discussed. Also the paper concentrates on various refinements done in area of EA to solve real life problems[19].
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