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 MAPCMASampler provides an implementation of the MAP-IGO (maximum a posteriori information geometric optimization) framework, which extends the CMA-ES rank-one-update. This sampler adds momentum-based updates to the standard CMA-ES, following the MAP-IGO algorithm.
Class or Function Names MAPCMASampler(mean: dict[str, Any] | None = None, sigma0: float | None = None, seed: int | None = None, popsize: int | None = None, cov: np.ndarray | None = None, momentum_r: float | None = None, search_space: dict[str, BaseDistribution] | None = None, independent_sampler: BaseSampler | None = None) mean: Initial mean of MAPCMA.
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.
APIs MOEADSampler(*, population_size = 100, n_neighbors = None, scalar_aggregation_func = "tchebycheff", mutation = None, mutation_prob = None, crossover = None, crossover_prob = 0.9, seed = None n_neighbors: The number of the weight vectors in the neighborhood of each weight vector.