Class or Function Names HEBOSampler Installation # Install the dependencies. pip install optunahub hebo # NOTE: Below is optional, but pymoo must be installed after NumPy for faster HEBOSampler, # we run the following command to make sure that the compiled version is installed. pip install --upgrade pymoo At the moment, for Python 3.12+, the following workaround is required to install HEBO.
pip install --no-deps --no-build-isolation hebo pip install optuna optunahub numpy pandas pymoo disjoint_set gpytorch APIs HEBOSampler(search_space: dict[str, BaseDistribution] | None = None, *, seed: int | None = None, constant_liar: bool = False, independent_sampler: BaseSampler | None = None, num_obj: int = 1) search_space: By specifying search_space, the sampling speed at each iteration becomes slightly quicker, but this argument is not necessary to run this sampler.
Abstract JanusMooCmixSampler is a plug-and-play, training-free multi-objective sampler that augments Optuna’s NSGA-II host with JANUS-style, geometry-guided candidate generation (arXiv:2608.22862).
Population optimizers such as NSGA-II drive search mainly through rank-based selection signals: a signal says one candidate dominates another, but not the local direction responsible for the improvement. JANUS recovers this missing local geometric signal without replacing the host optimizer. It estimates a local metric from the recent evaluation trace and occasionally replaces a child with inverse-curvature Gaussian samples drawn around the current Pareto front.
Abstract JanusSampler is a single-objective, plug-and-play sampler that augments a CMA-ES host (evotorch backend) with JANUS-style, geometry-guided candidate generation (arXiv:2608.22862). It is the single-objective counterpart of JanusMooCmixSampler.
Population optimizers such as CMA-ES drive search through rank-based selection: the signal says one candidate is better than another, but not the local direction responsible for the improvement. JANUS recovers this missing geometric signal from a single estimated Jacobian J and uses it for both halves of the search, without replacing the host optimizer: