NSGA2SteepestDescentSubsetGeneticAlgorithm#

class pybrops.opt.algo.NSGA2MemeticSubsetGeneticAlgorithm.NSGA2SteepestDescentSubsetGeneticAlgorithm(ngen=250, pop_size=100, phc=0.1, rng=RandomState(MT19937) at 0x7F8D0DF4C840, **kwargs)[source]#

Bases: SubsetOptimizationAlgorithm

Class implementing an NSGA-II genetic algorithm adapted for subset selection optimization. The search space is discrete and nominal in nature.

Constructor for NSGA-II subset optimization algorithm.

Parameters:

ngen (int) – Number of generations to evolve population.
mu (int) – Number of parental candidates to keep in population.
lamb (int) – Number of progeny to generate.
M (float) – Length of the chromosome genetic map, in Morgans.
rng (numpy.random.Generator, numpy.random.RandomState, None) – Random number generator source.
kwargs (dict) – Additional keyword arguments.

Methods

minimize

Optimize an objective function.

Attributes

`ngen`	Number of generations.
`phc`	Probability an individual experiences a hillclimb.
`pop_size`	Number of individuals in the main chromosome population.
`rng`	Random number generator source.

minimize(prob, miscout=None, **kwargs)[source]#

Optimize an objective function.

Parameters:

prob (SubsetProblem) – A problem definition object on which to optimize.
miscout (dict) – Miscellaneous output from the constrained optimizaiont algorithm.
kwargs (dict) – Additional keyword arguments

Returns:

out – An object containing the solution to the provided problem.

Return type:

SubsetSolution

property ngen: Integral#: Number of generations.

property phc: float#: Probability an individual experiences a hillclimb.

property pop_size: Integral#: Number of individuals in the main chromosome population.

property rng: Generator | RandomState#: Random number generator source.