import numpy as np # engine for numerical computing
from pypop7.optimizers.es.es import ES
[docs]class NES(ES):
"""Natural Evolution Strategies (NES).
This is the **abstract** class for all `NES` classes. Please use any of its instantiated subclasses to
optimize the black-box problem at hand.
.. note:: `NES` is a family of **principled** population-based randomized search methods, which maximize
the expected fitness along with (estimated) `natural gradients
<https://direct.mit.edu/neco/article-abstract/10/2/251/6143/Natural-Gradient-Works-Efficiently-in-Learning>`_.
In this library, we have converted it to the *minimization* problem, in accordance with other modules.
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/descendants, aka offspring population size (`int`),
* 'n_parents' - number of parents/ancestors, aka parental population size (`int`),
* 'mean' - initial (starting) point (`array_like`),
* if not given, it will draw a random sample from the uniform distribution whose search range is
bounded by `problem['lower_boundary']` and `problem['upper_boundary']`.
* 'sigma' - initial global step-size, aka mutation strength (`float`).
Attributes
----------
mean : `array_like`
initial (starting) point, aka mean of Gaussian search/sampling/mutation distribution.
n_individuals : `int`
number of offspring/descendants, aka offspring population size.
n_parents : `int`
number of parents/ancestors, aka parental population size.
sigma : `float`
global step-size, aka mutation strength (i.e., overall std of Gaussian search distribution).
Methods
-------
References
----------
Wierstra, D., Schaul, T., Glasmachers, T., Sun, Y., Peters, J. and Schmidhuber, J., 2014.
Natural evolution strategies.
Journal of Machine Learning Research, 15(1), pp.949-980.
https://jmlr.org/papers/v15/wierstra14a.html
Schaul, T., 2011.
Studies in continuous black-box optimization.
Doctoral Dissertation, Technische Universität München.
https://people.idsia.ch/~schaul/publications/thesis.pdf
Yi, S., Wierstra, D., Schaul, T. and Schmidhuber, J., 2009, June.
Stochastic search using the natural gradient.
In Proceedings of International Conference on Machine Learning (pp. 1161-1168).
https://dl.acm.org/doi/10.1145/1553374.1553522
Wierstra, D., Schaul, T., Peters, J. and Schmidhuber, J., 2008, June.
Natural evolution strategies.
In IEEE Congress on Evolutionary Computation (pp. 3381-3387). IEEE.
https://ieeexplore.ieee.org/abstract/document/4631255
https://github.com/pybrain/pybrain
"""
def __init__(self, problem, options):
ES.__init__(self, problem, options)
self._u = None # for fitness shaping
def initialize(self):
r, _u = np.arange(self.n_individuals), np.zeros((self.n_individuals,))
for i in range(self.n_individuals):
if r[i] >= self.n_individuals*0.5:
_u[i] = r[i] - self.n_individuals*0.5
self._u = _u/np.max(_u)
def iterate(self):
raise NotImplementedError