Numerous real-world applications involve large-scale multi-objective optimization problems (LSMOPs) with hundreds or even thousands of decision variables. Although multi-objective evolutionary ...

Two of artificial intelligence’s most powerful yet fundamentally different tools are quietly merging, and a new open-access scientometric review has, for the first time, mapped exactly how fast, how b ...

Evolutionary algorithms form a robust class of metaheuristic methods inspired by natural selection, designed to tackle combinatorial optimisation tasks where the search space grows factorially or ...

Optimization problems rarely have a single right answer. In engineering design, scheduling, and machine learning, decision ...

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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A team led by Prof Frank Glorius from the Institute of Organic Chemistry at the University of Münster has developed an evolutionary algorithm that identifies the structures in a molecule that are ...

Researchers from Tokyo Metropolitan University have used an evolutionary algorithm to identify optimal shapes for ultra-thin, ...

Researchers from Tokyo Metropolitan University have used an evolutionary algorithm to identify optimal shapes for ultra-thin ...