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Evolutionary Algorithms are the stochastic optimization methods, simulating the behavior of natural evolution. These algorithms are basically population based search procedures efficiently dealing with complex search spaces having robust and powerful search mechanism. EAs are highly applicable in multiobjective optimization problem which are having conflicting objectives. This paper reviews the work carried out for diversity and convergence issues in EMO.
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Yagyasen et al. (2013) studied this question.
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