Adaptive Estimation of Multivariate Normal Algorithm (AEMNA)

class pypop7.optimizers.eda.aemna.AEMNA(problem, options)

Adaptive Estimation of Multivariate Normal Algorithm (AEMNA).

Note

AEMNA learns the full covariance matrix of the Gaussian sampling distribution, resulting in a cubic time complexity w.r.t. each sampling. Therefore, now it is rarely used for large-scale black-box optimization (LBO). It is highly recommended to first attempt other more advanced optimization methods for LBO.

Parameters:

• problem (dict) –

  problem arguments with the following common settings (keys):

  • ’fitness_function’ - objective function to be minimized (func),

  • ’ndim_problem’ - number of dimensionality (int),

  • ’upper_boundary’ - upper boundary of search range (array_like),

  • ’lower_boundary’ - lower boundary of search range (array_like).

• options (dict) –

  optimizer options with the following common settings (keys):

  • ’max_function_evaluations’ - maximum of function evaluations (int, default: np.inf),

  • ’max_runtime’ - maximal runtime (float, default: np.inf),

  • ’seed_rng’ - seed for random number generation needed to be explicitly set (int);

  and with the following particular settings (keys):

  • ’n_individuals’ - number of offspring, aka offspring population size (int, default: 200),

  • ’n_parents’ - number of parents, aka parental population size (int, default: int(options[‘n_individuals’]/2)).

Examples

Use the optimizer to minimize the well-known test function Rosenbrock:

>>> import numpy  # engine for numerical computing
>>> from pypop7.benchmarks.base_functions import rosenbrock  # function to be minimized
>>> from pypop7.optimizers.eda.aemna import AEMNA
>>> problem = {'fitness_function': rosenbrock,  # define problem arguments
...            'ndim_problem': 2,
...            'lower_boundary': -5*numpy.ones((2,)),
...            'upper_boundary': 5*numpy.ones((2,))}
>>> options = {'max_function_evaluations': 5000,  # set optimizer options
...            'seed_rng': 2022}
>>> aemna = AEMNA(problem, options)  # initialize the optimizer class
>>> results = aemna.optimize()  # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"AEMNA: {results['n_function_evaluations']}, {results['best_so_far_y']}")
AEMNA: 5000, 0.0023607608362747035

For its correctness checking of coding, refer to this code-based repeatability report for more details.

n_individuals

number of offspring, aka offspring population size.

Type:

int

n_parents

number of parents, aka parental population size.

Type:

int

References

Larrañaga, P. and Lozano, J.A. eds., 2002. Estimation of distribution algorithms: A new tool for evolutionary computation. Springer Science & Business Media.