CMA-ES

class pints.CMAES(x0, sigma0=None, boundaries=None)

Finds the best parameters using the CMA-ES method described in [1], [2] and implemented in the cma module [3].

CMA-ES stands for Covariance Matrix Adaptation Evolution Strategy, and is designed for non-linear derivative-free optimization problems.

To initialise, set an initial position x0, an initial covariance sigma0, and optionally a set of pints.Boundaries.

CMA-ES is designed for multivariate optimisation, so x0 should have a dimension of 2 or greater.

The initial covariance sigma0 should be a scalar (one standard deviation for all parameters), but for compatibility with other optimisers an array is also accepted: in this case the smallest value in sigma0 will be used. If no sigma0 is given a guess will be made based on the boundaries (if provided) or x0, see Optimiser() for details. The method’s authors suggest choosing sigma0 such that the optimum is expected to be within 3 * sigma0 from the initial position x0.

Rectangular boundaries are supported by the cma module natively. Other boundary shapes are also supported but will be handled by PINTS (which will pass nan to cma for any point requested outside of the boundaries).

Extends PopulationBasedOptimiser.

References

[1]

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

[2]

Hansen, Mueller, Koumoutsakos (2006) “Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES)”. Evolutionary Computation https://doi.org/10.1162/106365603321828970

[3]

PyPi page for cma https://pypi.org/project/cma/

ask()

See Optimiser.ask().

f_best()

See Optimiser.f_best().

f_guessed()

See Optimiser.f_guessed().

fbest()

Deprecated alias of f_best().

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().