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—The difficulties faced by existing Multi-objective Evolutionary Algorithms (MOEAs) in handling many-objective problems relate to the inefficiency of selection operators, high computational cost and difficulty in visualization of objective space. While many approaches aim to counter these difficulties by increasing the fidelity of the standard selection(More)
Test problems have played a fundamental role in understanding the strengths and weaknesses of the existing Evolutionary Multi-objective Optimization (EMO) algorithms. A range of test problems exist which have enabled the research community to understand how the performance of EMO algorithms is affected by the geometrical shape of the Pareto front (PF),(More)
Keywords: Evolutionary many-objective optimization Principal component analysis Maximum variance unfolding Kernels and multiple criteria decision-making a b s t r a c t Multiple Criteria Decision-Making (MCDM) based Multi-objective Evolutionary Algorithms (MOEAs) are increasingly becoming popular for dealing with optimization problems with more than three(More)
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