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 Sampler using MOEA/D algorithm. MOEA/D stands for “Multi-Objective Evolutionary Algorithm based on Decomposition.
This sampler is specialized for multiobjective optimization. The objective function is internally decomposed into multiple single-objective subproblems to perform optimization.
It may not work well with multi-threading. Check results carefully.
Class or Function Names MOEADSampler Installation pip install scipy or
pip install -r https://hub.optuna.org/samplers/moead/requirements.txt Example import optuna import optunahub def objective(trial: optuna.Trial) -> tuple[float, float]: x = trial.