Bare-bones CMA-ES

class pints.BareCMAES(x0, sigma0=0.1, boundaries=None)

Finds the best parameters using the CMA-ES method described in [1, 2], using a bare bones re-implementation.

For general use, we recommend the pints.CMAES optimiser, which wraps around the cma module provided by the authors of CMA-ES. The cma module provides a battle-tested version of the optimiser.

The role of this class, is to provide a simpler implementation of only the core algorithm of CMA-ES, which is easier to read and analyse, and which can be used to compare with bare implementations of other methods.

Extends PopulationBasedOptimiser.

References

[1]

The CMA Evolution Strategy: A Tutorial Nikolaus Hanse, arxiv https://arxiv.org/abs/1604.00772

[2]

Hansen, Mueller, Koumoutsakos (2003) “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)”. Evolutionary Computation https://doi.org/10.1162/106365603321828970

ask()

See Optimiser.ask().

cov(decomposed=False)

Returns the current covariance matrix C of the proposal distribution.

If the optional argument decomposed is set to True, a tuple (R, S) will be returned such that R contains the eigenvectors of C while S is a diagonal matrix containing the squares of the eigenvalues of C, such that C = R S S R.T.

f_best()

See Optimiser.f_best().

f_guessed()

See Optimiser.f_guessed().

fbest()

Deprecated alias of f_best().

mean()

Returns the current mean of the proposal distribution.

n_hyper_parameters()

See TunableMethod.n_hyper_parameters().

classmethod name()

See Optimiser.name().

needs_sensitivities()

Returns True if this methods needs sensitivities to be passed in to tell along with the evaluated error.

population_size()

Returns this optimiser’s population size.

If no explicit population size has been set, None may be returned. Once running, the correct value will always be returned.

running()

See Optimiser.running().

set_hyper_parameters(x)

The hyper-parameter vector is [population_size].

See TunableMethod.set_hyper_parameters().

set_population_size(population_size=None)

Sets a population size to use in this optimisation.

If population_size is set to None, the population size will be set using the heuristic suggested_population_size().

stop()

See Optimiser.stop().

suggested_population_size(round_up_to_multiple_of=None)

Returns a suggested population size for this method, based on the dimension of the search space (e.g. the parameter space).

If the optional argument round_up_to_multiple_of is set to an integer greater than 1, the method will round up the estimate to a multiple of that number. This can be useful to obtain a population size based on e.g. the number of worker processes used to perform objective function evaluations.

tell(fx)

See Optimiser.tell().

x_best()

See Optimiser.x_best().

x_guessed()

See Optimiser.x_guessed().

xbest()

Deprecated alias of x_best().