import numpy as np # engine for numerical computing
from pypop7.optimizers.core.optimizer import Optimizer
[docs]class EP(Optimizer):
"""Evolutionary Programming (EP).
This is the **abstract** class for all `EP` classes. Please use any of its instantiated subclasses to
optimize the black-box problem at hand.
.. note:: `EP` is one of three classical families of evolutionary algorithms (EAs), proposed originally by Lawrence
J. Fogel, the recipient of `IEEE Evolutionary Computation Pioneer Award 1998 <https://tinyurl.com/456as566>`_ and
`IEEE Frank Rosenblatt Award 2006 <https://tinyurl.com/yj28zxfa>`_. When used for continuous optimization, most
of modern `EP` versions share much similarities (e.g. self-adaptation) with `ES
<https://pypop.readthedocs.io/en/latest/es/es.html>`_, another representative EA.
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`).
Attributes
----------
n_individuals : `int`
number of offspring, aka offspring population size.
sigma : `float`
initial global step-size, aka mutation strength.
Methods
-------
References
----------
Lee, C.Y. and Yao, X., 2004.
Evolutionary programming using mutations based on the Lévy probability distribution.
IEEE Transactions on Evolutionary Computation, 8(1), pp.1-13.
https://ieeexplore.ieee.org/document/1266370
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
Fogel, D.B., 1999.
An overview of evolutionary programming.
In Evolutionary Algorithms (pp. 89-109). Springer, New York, NY.
https://link.springer.com/chapter/10.1007/978-1-4612-1542-4_5
Fogel, D.B. and Fogel, L.J., 1995, September.
An introduction to evolutionary programming.
In European Conference on Artificial Evolution (pp. 21-33). Springer, Berlin, Heidelberg.
https://link.springer.com/chapter/10.1007/3-540-61108-8_28
Fogel, D.B., 1994.
An introduction to simulated evolutionary optimization.
IEEE Transactions on Neural Networks, 5(1), pp.3-14.
https://ieeexplore.ieee.org/abstract/document/265956
Fogel, D.B., 1994.
Evolutionary programming: An introduction and some current directions.
Statistics and Computing, 4(2), pp.113-129.
https://link.springer.com/article/10.1007/BF00175356
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
"""
def __init__(self, problem, options):
Optimizer.__init__(self, problem, options)
if self.n_individuals is None:
self.n_individuals = 100 # number of offspring, aka offspring population size
self.sigma = options.get('sigma') # initial global step-size, aka mutation strength
self._n_generations = 0 # number of generations
self._printed_evaluations = 0 # only for printing
def initialize(self):
raise NotImplementedError
def iterate(self):
raise NotImplementedError
def _print_verbose_info(self, fitness, y, is_print=False):
if y is not None and self.saving_fitness:
if not np.isscalar(y):
fitness.extend(y)
else:
fitness.append(y)
if self.verbose:
is_verbose = self._printed_evaluations != self.n_function_evaluations # to avoid repeated printing
is_verbose_1 = (not self._n_generations % self.verbose) and is_verbose
is_verbose_2 = self.termination_signal > 0 and is_verbose
is_verbose_3 = is_print and is_verbose
if is_verbose_1 or is_verbose_2 or is_verbose_3:
info = ' * Generation {:d}: best_so_far_y {:7.5e}, min(y) {:7.5e} & Evaluations {:d}'
print(info.format(self._n_generations, self.best_so_far_y, np.min(y), self.n_function_evaluations))
self._printed_evaluations = self.n_function_evaluations
def _collect(self, fitness=None, y=None):
self._print_verbose_info(fitness, y)
results = Optimizer._collect(self, fitness)
results['_n_generations'] = self._n_generations
return results