cma.transformations.GenoPheno

class documentation

class GenoPheno(object):

Genotype-phenotype transformation.

Method pheno provides the transformation from geno- to phenotype, that is from the internal representation to the representation used in the objective function. Method geno provides the "inverse" pheno- to genotype transformation. The geno-phenotype transformation comprises, in this order:

insert fixed variables (with the phenotypic values)

affine linear transformation (first scaling then shift)

user-defined transformation

repair (e.g. into feasible domain due to boundaries)

re-assign fixed variables their original phenotypic value

By default all transformations are the identity. The repair is only applied, if the transformation is given as argument to the method pheno.

geno is only necessary, if solutions have been injected.

Method	`__init__`	return `GenoPheno` instance with phenotypic dimension `dim`.
Method	`geno`	maps the phenotypic input argument into the genotypic space, that is, computes essentially the inverse of `pheno`.
Method	`pheno`	maps the genotypic input argument into the phenotypic space, see help for class `GenoPheno`
Instance Variable	`fixed_values`	Undocumented
Instance Variable	`isidentity`	Undocumented
Instance Variable	`islinear`	Undocumented
Instance Variable	`N`	Undocumented
Instance Variable	`repaired_solutions`	Undocumented
Instance Variable	`scales`	Undocumented
Instance Variable	`tf_geno`	Undocumented
Instance Variable	`tf_pheno`	Undocumented
Instance Variable	`typical_x`	Undocumented

def __init__(self, dim, scaling=None, typical_x=None, fixed_values=None, tf=None): ¶

return GenoPheno instance with phenotypic dimension dim.

Keyword Arguments

scaling

the diagonal of a scaling transformation matrix, multipliers in the genotyp-phenotyp transformation, see typical_x

typical_x

pheno = scaling*geno + typical_x

fixed_values

a dictionary of variable indices and values, like {0:2.0, 2:1.1}, that are not subject to change, negative indices are ignored (they act like incommenting the index), values are phenotypic values.

tf
list of two user-defined transformation functions, or None.

tf[0] is a function that transforms the internal representation as used by the optimizer into a solution as used by the objective function. tf[1] does the back-transformation. For example:
tf_0 = lambda x: [xi**2 for xi in x]
tf_1 = lambda x: [abs(xi)**0.5 fox xi in x]
or "equivalently" without the lambda construct:
def tf_0(x):
    return [xi**2 for xi in x]
def tf_1(x):
    return [abs(xi)**0.5 fox xi in x]
tf=[tf_0, tf_1] is a reasonable way to guaranty that only positive values are used in the objective function.

Details

If tf_0 is not the identity and tf_1 is ommitted, the genotype of x0 cannot be computed consistently and "injection" of phenotypic solutions is likely to lead to unexpected results.

def geno(self, y, from_bounds=None, copy=True, repair=None, archive=None): ¶

maps the phenotypic input argument into the genotypic space, that is, computes essentially the inverse of pheno.

By default a copy is made only to prevent to modify y.

The inverse of the user-defined transformation (if any) is only needed if external solutions are injected, it is not applied to the initial solution x0.

Details

geno searches first in archive for the genotype of y and returns the found value, typically unrepaired. Otherwise, first from_bounds is applied, to revert a projection into the bound domain (if necessary) and pheno is reverted. repair is applied last, and is usually the method CMAEvolutionStrategy.repair_genotype that limits the Mahalanobis norm of geno(y) - mean.

def pheno(self, x, into_bounds=None, copy=True, archive=None, iteration=None): ¶

maps the genotypic input argument into the phenotypic space, see help for class GenoPheno

Details

If copy, values from x are copied if changed under the transformation.

fixed_values = ¶

Undocumented

isidentity: bool = ¶

Undocumented

islinear: bool = ¶

Undocumented

N = ¶

Undocumented

repaired_solutions = ¶

Undocumented

scales: int = ¶

Undocumented

tf_geno = ¶

Undocumented

tf_pheno = ¶

Undocumented

typical_x: int = ¶

Undocumented