Classical Evolutionary Programming (CEP)

class pypop7.optimizers.ep.cep.CEP(problem, options)

Classical Evolutionary Programming with self-adaptive mutation (CEP).

Note

To obtain satisfactory performance for large-scale black-box optimization, the number of offspring (n_individuals) and also initial global step-size (sigma) may need to be carefully tuned (e.g. via manual trial-and-error or automatical hyper-parameter optimization).

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 to be allowed (float, default: np.Inf),

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

  and with the following particular settings (keys):

  • ’sigma’ - initial global step-size, aka mutation strength (float),

  • ’n_individuals’ - number of offspring, aka offspring population size (int, default: 100),

  • ’q’ - number of opponents for pairwise comparisons (int, default: 10),

  • ’tau’ - learning rate of individual step-sizes self-adaptation (float, default: 1.0/np.sqrt(2.0*np.sqrt(problem[‘ndim_problem’]))),

  • ’tau_apostrophe’ - learning rate of individual step-sizes self-adaptation (float, default: 1.0/np.sqrt(2.0*problem[‘ndim_problem’]).

Examples

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

>>> import numpy
>>> from pypop7.benchmarks.base_functions import rosenbrock  # function to be minimized
>>> from pypop7.optimizers.ep.cep import CEP
>>> 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,
...            'sigma': 0.1}
>>> cep = CEP(problem, options)  # initialize the optimizer class
>>> results = cep.optimize()  # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"CEP: {results['n_function_evaluations']}, {results['best_so_far_y']}")
CEP: 5000, 0.3544823323771589

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

n_individuals

number of offspring, aka offspring population size.

Type:

int

q

number of opponents for pairwise comparisons.

Type:

int

sigma

initial global step-size, aka mutation strength.

Type:

float

tau

learning rate of individual step-sizes self-adaptation.

Type:

float

tau_apostrophe

learning rate of individual step-sizes self-adaptation.

Type:

float

References

Yao, X., Liu, Y. and Lin, G., 1999. Evolutionary programming made faster. IEEE Transactions on Evolutionary Computation, 3(2), pp.82-102. https://ieeexplore.ieee.org/abstract/document/771163

Bäck, T. and Schwefel, H.P., 1993. An overview of evolutionary algorithms for parameter optimization. Evolutionary Computation, 1(1), pp.1-23. https://direct.mit.edu/evco/article-abstract/1/1/1/1092/An-Overview-of-Evolutionary-Algorithms-for