Abstract MoCmaSampler provides the implementation of the s-MO-CMA-ES algorithm. This algorithm extends (1+1)-CMA-ES to multi-objective optimization by introducing a selection strategy based on non-domination sorting and contributing hypervolume (S-metric). It inherits important properties of CMA-ES, invariance against order-preserving transformations of the fitness function value and rotation and translation of the search space.
Class or Function Names MoCmaSampler(*, search_space: dict[str, BaseDistribution] | None = None, popsize: int | None = None, seed: int | None = None) search_space: A dictionary containing the search space that defines the parameter space.