comocma.como.CmaKernel : API documentation

def __init__(self, x0, sigma0, inopts=None):

x0

initial solution, starting point. x0 is given as "phenotype" which means, if:

opts = {'transformation': [transform, inverse]}

is given and inverse is None, the initial mean is not consistent with x0 in that transform(mean) does not equal to x0 unless transform(mean) equals mean.

sigma0

initial standard deviation. The problem variables should have been scaled, such that a single standard deviation on all variables is useful and the optimum is expected to lie within about x0 +- 3*sigma0. See also options scaling_of_variables. Often one wants to check for solutions close to the initial point. This allows, for example, for an easier check of consistency of the objective function and its interfacing with the optimizer. In this case, a much smaller sigma0 is advisable.

inopts

options, a dictionary with optional settings, see class cma.CMAOptions.

@property
def incumbent(self):

it gives the 'repaired' mean of a cma-es. For a problem with bound constraints, self.incumbent in inside the bounds.

def _copy_light(self, sigma=None, inopts=None):

tentative copy of self, versatile (interface and functionalities may change).

This may not work depending on the used sampler.

Copy mean and sample distribution parameters and input options.

Do not copy evolution paths, termination status or other state variables.

Method	__init__	No summary
Method	incumbent	it gives the 'repaired' mean of a cma-es. For a problem with bound constraints, `self.incumbent` in inside the bounds.
Method	_copy_light	tentative copy of self, versatile (interface and functionalities may change).

comocma.como.CmaKernel(cma.CMAEvolutionStrategy) class documentation

`comocma.como.CmaKernel(cma.CMAEvolutionStrategy)` class documentation