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In the design of evolutionary multiobjective optimization (EMO) algorithms, it is important to strike a balance between diversity and convergence. Traditional mask-based crossover operators for binary strings (e.g., one-point, two-point, and uniform) tend to decrease the spread of solutions along the Pareto front in EMO algorithms while they improve the convergence to part of the Pareto front. This...
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