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Found 8 papers, 4 papers with code
CMA-ES for Discrete and Mixed-Variable Optimization on Sets of Points
Discrete and mixed-variable optimization problems have appeared in several real-world applications.
Tail Bounds on the Runtime of Categorical Compact Genetic Algorithm
We term this specific algorithm the categorical compact genetic algorithm (ccGA).
Natural Gradient Interpretation of Rank-One Update in CMA-ES
While the rank-one update makes the covariance matrix to increase the likelihood of generating a solution in the direction of the evolution path, this idea has been difficult to formulate and interpret as a natural gradient method unlike the rank-$\mu$ update.
CMA-ES for Safe Optimization
This optimization setting is known as safe optimization and formulated as a specialized type of constrained optimization problem with constraints for safety functions.
CatCMA : Stochastic Optimization for Mixed-Category Problems
CatCMA updates the parameters of the joint probability distribution in the natural gradient direction.
(1+1)-CMA-ES with Margin for Discrete and Mixed-Integer Problems
The margin correction has been applied to ($\mu/\mu_\mathrm{w}$,$\lambda$)-CMA-ES, while this paper introduces the margin correction into (1+1)-CMA-ES, an elitist version of CMA-ES.
Marginal Probability-Based Integer Handling for CMA-ES Tackling Single-and Multi-Objective Mixed-Integer Black-Box Optimization
If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization.
CMA-ES with Margin: Lower-Bounding Marginal Probability for Mixed-Integer Black-Box Optimization
If the CMA-ES is applied to the MI-BBO with straightforward discretization, however, the variance corresponding to the integer variables becomes much smaller than the granularity of the discretization before reaching the optimal solution, which leads to the stagnation of the optimization.
