Abstract The cutting-edge evolutionary computation algorithm CatCMA has been published on OptunaHub. CatCMA is an algorithm that excels in mixed search spaces with continuous and discrete variables.
This figure is from https://arxiv.org/abs/2405.09962.
📝 Introduction to CatCMA in OptunaHub: Blog post by Hideaki Imamura.
Class or Function Names CatCmaSampler Installation pip install -r https://hub.optuna.org/samplers/catcma/requirements.txt Example import numpy as np import optuna from optuna.distributions import CategoricalDistribution from optuna.distributions import FloatDistribution import optunahub def objective(trial: optuna.
Abstract Differential Evolution (DE) Sampler This implementation introduces a novel Differential Evolution (DE) sampler, tailored to optimize both numerical and categorical hyperparameters effectively. The DE sampler integrates a hybrid approach:
Differential Evolution for Numerical Parameters: Exploiting DE’s strengths, the sampler efficiently explores numerical parameter spaces through mutation, crossover, and selection mechanisms. Random Sampling for Categorical Parameters: For categorical variables, the sampler employs random sampling, ensuring comprehensive coverage of discrete spaces. The sampler also supports dynamic search spaces, enabling seamless adaptation to varying parameter dimensions during optimization.
Abstract OptQuest developed by OptTek Systems, Inc. is a black-box solver that has been in development for over 30 years and has hundreds of thousands of licensed users across all industries. The OptQuest optimization engine implements black-box optimization through a metaheuristic framework that orchestrates multiple lower-level heuristics without relying on explicit problem formulations, gradients, or assumptions about convexity or smoothness. Metaheuristics operate as high-level, problem-independent strategies that dynamically combine and guide heuristics to balance exploration and exploitation in large search spaces: scatter search generates new candidates from combinations of elite solutions, tailored to variable types including continuous, integer, binary, discrete, categorical, and permutation; tabu search employs short- and long-term memory structures to avoid revisiting recent or poor regions and escape local optima; population-based approaches such as genetic algorithms evolve solution sets via fitness-based selection, recombination, and mutation; particle swarm optimization updates solution trajectories using velocity vectors influenced by personal and global bests with constriction for convergence; and surrogate models are constructed from evaluated points to predict and guide toward promising areas.
Abstract CatCMA with Margin [Hamano et al. 2025] CatCMA with Margin (CatCMAwM) is a method for mixed-variable optimization problems, simultaneously optimizing continuous, integer, and categorical variables. CatCMAwM extends CatCMA by introducing a novel integer handling mechanism, and supports arbitrary combinations of continuous, integer, and categorical variables in a unified framework. This Optuna sampler uses https://github.com/CyberAgentAILab/cmaes under the hood, so please refer to it for details.
APIs CatCmawmSampler
Example from __future__ import annotations import numpy as np import optuna import optunahub def SphereIntCOM(x: np.