Drift analysis on a mixed-integer benchmark shows (1+1)-LB-ES risks premature convergence with large numbers of integer variables while (1+1)-LUB-ES achieves linear convergence after integers are fixed under suitable bounds.
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Evolutionary algorithms can discover molecules with improved nonlinear optical properties by simultaneously optimizing hyperpolarizability ratio, HOMO-LUMO gap, polarizability, and energy per atom.
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Convergence Analysis of Evolution Strategies for Mixed-Integer Optimization
Drift analysis on a mixed-integer benchmark shows (1+1)-LB-ES risks premature convergence with large numbers of integer variables while (1+1)-LUB-ES achieves linear convergence after integers are fixed under suitable bounds.
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Multi-Objective Evolutionary Design of Molecules with Enhanced Nonlinear Optical Properties
Evolutionary algorithms can discover molecules with improved nonlinear optical properties by simultaneously optimizing hyperpolarizability ratio, HOMO-LUMO gap, polarizability, and energy per atom.