import numpy as np # engine for numerical computing
from pypop7.optimizers.core.optimizer import Optimizer
[docs]class EDA(Optimizer):
"""Estimation of Distribution Algorithms (EDA).
This is the **abstract** class for all `EDA` classes. Please use any of its instantiated subclasses to
optimize the black-box problem at hand.
.. note:: *`EDA` are a modern branch of evolutionary algorithms with some unique advantages in
principle*, as recognized in `[Kabán et al., 2016, ECJ] <https://tinyurl.com/mrxpe28y>`_.
AKA `probabilistic model-building genetic algorithms (PMBGA)
<https://link.springer.com/article/10.1023/B:NACO.0000023416.59689.4e>`_, `iterated density estimation
evolutionary algorithms (IDEA)
<https://dspace.library.uu.nl/bitstream/handle/1874/1886/2000-15.pdf?sequence=1>`_.
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`):
* 'n_individuals' - number of offspring, aka offspring population size (`int`, default: `200`),
* 'n_parents' - number of parents, aka parental population size (`int`, default:
`int(self.n_individuals/2)`).
Attributes
----------
n_individuals : `int`
number of offspring, aka offspring population size.
n_parents : `int`
number of parents, aka parental population size.
Methods
-------
References
----------
https://www.dagstuhl.de/en/program/calendar/semhp/?semnr=22182
Brookes, D., Busia, A., Fannjiang, C., Murphy, K. and Listgarten, J., 2020, July.
A view of estimation of distribution algorithms through the lens of expectation-maximization.
In Proceedings of Genetic and Evolutionary Computation Conference Companion (pp. 189-190). ACM.
https://dl.acm.org/doi/abs/10.1145/3377929.3389938
Kabán, A., Bootkrajang, J. and Durrant, R.J., 2016.
Toward large-scale continuous EDA: A random matrix theory perspective.
Evolutionary Computation, 24(2), pp.255-291.
https://direct.mit.edu/evco/article-abstract/24/2/255/1016/Toward-Large-Scale-Continuous-EDA-A-Random-Matrix
Larrañaga, P. and Lozano, J.A. eds., 2002.
Estimation of distribution algorithms: A new tool for evolutionary computation.
Springer Science & Business Media.
https://link.springer.com/book/10.1007/978-1-4615-1539-5
([Jose Lozano: IEEE Fellow for contributions to EDAs](https://tinyurl.com/sssfsfw8))
Mühlenbein, H. and Mahnig, T., 2001.
Evolutionary algorithms: From recombination to search distributions.
In Theoretical Aspects of Evolutionary Computing (pp. 135-173). Springer, Berlin, Heidelberg.
https://link.springer.com/chapter/10.1007/978-3-662-04448-3_7
Berny, A., 2000, September.
Selection and reinforcement learning for combinatorial optimization.
In International Conference on Parallel Problem Solving from Nature (pp. 601-610).
Springer, Berlin, Heidelberg.
https://link.springer.com/chapter/10.1007/3-540-45356-3_59
Bosman, P.A. and Thierens, D., 2000, September.
Expanding from discrete to continuous estimation of distribution algorithms: The IDEA.
In International Conference on Parallel Problem Solving from Nature (pp. 767-776).
Springer, Berlin, Heidelberg.
https://link.springer.com/chapter/10.1007/3-540-45356-3_75
Mühlenbein, H., 1997.
The equation for response to selection and its use for prediction.
Evolutionary Computation, 5(3), pp.303-346.
https://tinyurl.com/yt78c786
Baluja, S. and Caruana, R., 1995.
Removing the genetics from the standard genetic algorithm.
In International Conference on Machine Learning (pp. 38-46). Morgan Kaufmann.
https://www.sciencedirect.com/science/article/pii/B9781558603776500141
"""
def __init__(self, problem, options):
Optimizer.__init__(self, problem, options)
if self.n_individuals is None: # number of offspring, aka offspring population size
self.n_individuals = 200
if self.n_parents is None: # number of parents, aka parental population size
self.n_parents = int(self.n_individuals/2)
self._n_generations = 0
self._n_restart = 0
self._printed_evaluations = self.n_function_evaluations
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
results['_n_restart'] = self._n_restart
return results