Numerous real-world applications involve large-scale multi-objective optimization problems (LSMOPs) with hundreds or even thousands of decision variables. Although multi-objective evolutionary ...
A new ensemble machine learning-based prediction mechanism reinitializes evolutionary algorithm populations after ...
A new review maps the evolution of cloud task scheduling algorithms, from classical heuristics to nature-inspired ...
Expensive optimization problem (EOP) refers to the problem that requires expensive or even unaffordable costs to evaluate candidate solutions, which widely exist in many significant real-world ...
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