cmaimplements the CMA-ES (Covariance Matrix Adaptation Evolution Strategy).
bbobbenchmarks- BBOB noiseless testbed.
constraints_handler- A collection of boundary and (in future) constraints handling classes.
evolution_strategy- CMA-ES (evolution strategy), the main sub-module of
cmaproviding in particular
fitness_functions- versatile container for test objective functions.
fitness_models- Fitness surrogate model classes and handler for incremental evaluations.
fitness_transformations- Wrapper for objective functions like noise, rotation, gluing args
interfaces- Very few interface defining base class definitions
logger- logger class mainly to be used with
optimization_tools- Utility classes and functionalities loosely related to optimization
purecma- A minimalistic implemention of CMA-ES without using
RecombinationWeightsis a list of recombination weights for the CMA-ES.
restricted_gaussian_sampler- VD-CMA and VkD-CMA
s- versatile shortcuts for quick typing in an (i)python shell or even from cma.s import * in interactive sessions.
sampler- Collection of classes that sample from parametrized distributions and provide an update mechanism of the distribution parameters.
sigma_adaptation- step-size adaptation classes, currently tightly linked to CMA, because
hsigis computed in the base class
test- test module of
transformations- Search space transformation and encoding/decoding classes
utilities- various unspecific utilities
wrapper- Interface wrappers for the