import numpy as np
from pypop7.optimizers.cc.cc import CC
[docs]class COSYNE(CC):
"""CoOperative SYnapse NEuroevolution (COSYNE).
.. note:: This is a wrapper of `COSYNE`, which has been implemented in the Python library `EvoTorch
<https://docs.evotorch.ai/v0.3.0/reference/evotorch/algorithms/ga/#evotorch.algorithms.ga.Cosyne>`_,
with slight modifications.
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 for Gaussian search distribution (`float`),
* 'n_individuals' - number of individuals/samples, aka population size (`int`, default: `100`),
* 'n_tournaments' - number of tournaments for one-point crossover (`int`, default: `10`),
* 'ratio_elitists' - ratio of elitists (`float`, default: `0.3`).
Examples
--------
Use the optimizer to minimize the well-known test function
`Rosenbrock <http://en.wikipedia.org/wiki/Rosenbrock_function>`_:
.. code-block:: python
:linenos:
>>> import numpy
>>> from pypop7.benchmarks.base_functions import rosenbrock # function to be minimized
>>> from pypop7.optimizers.cc.cosyne import COSYNE
>>> 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': 0.3,
... 'x': 3*numpy.ones((2,))}
>>> cosyne = COSYNE(problem, options) # initialize the optimizer class
>>> results = cosyne.optimize() # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"COSYNE: {results['n_function_evaluations']}, {results['best_so_far_y']}")
COSYNE: 5000, 0.005023488269997175
For its correctness checking of coding, refer to `this code-based repeatability report
<https://tinyurl.com/yff8c6xu>`_ for more details.
Attributes
----------
n_individuals : `int`
number of individuals/samples, aka population size.
n_tournaments : `int`
number of tournaments for one-point crossover.
ratio_elitists : `float`
ratio of elitists.
sigma : `float`
initial global step-size for Gaussian search (mutation/sampling) distribution.
References
----------
Gomez, F., Schmidhuber, J. and Miikkulainen, R., 2008.
Accelerated neural evolution through cooperatively coevolved synapses.
Journal of Machine Learning Research, 9(31), pp.937-965.
https://jmlr.org/papers/v9/gomez08a.html
https://docs.evotorch.ai/v0.3.0/reference/evotorch/algorithms/ga/#evotorch.algorithms.ga.Cosyne
https://github.com/nnaisense/evotorch/blob/master/src/evotorch/algorithms/ga.py
"""
def __init__(self, problem, options):
CC.__init__(self, problem, options)
self.sigma = options.get('sigma') # global step-size for Gaussian search distribution
self.n_tournaments = options.get('n_tournament', 10) # number of tournaments for one-point crossover
self.ratio_elitists = options.get('ratio_elitists', 0.3) # ratio of elitists
self._n_elitists = int(self.ratio_elitists*self.n_individuals) # number of elitists
self._n_parents = int(self.n_individuals/4) # parents for crossover and mutation
def initialize(self, args=None, is_restart=False):
x = self.rng_initialization.uniform(self.initial_lower_boundary, self.initial_upper_boundary,
size=(self.n_individuals, self.ndim_problem)) # population
y = np.empty((self.n_individuals,)) # fitness
for i in range(self.n_individuals):
if self._check_terminations():
return x, y
y[i] = self._evaluate_fitness(x[i], args)
return x, y
def _crossover(self, x, y): # one-point crossover
xx = np.empty((2*len(y), self.ndim_problem))
for i in range(len(y)):
left = self.rng_optimization.choice(len(y), size=(self.n_tournaments,), replace=False)
left = x[np.argmin(y[left])]
right = self.rng_optimization.choice(len(y), size=(self.n_tournaments,), replace=False)
right = x[np.argmin(y[right])]
p = self.rng_optimization.choice(self.ndim_problem)
xx[2*i], xx[2*i + 1] = np.append(left[:p], right[p:]), np.append(right[:p], left[p:])
return xx
def _mutate(self, x): # mutation for all dimensions
x += self.sigma*self.rng_optimization.standard_normal(size=(x.shape[0], self.ndim_problem))
x = np.clip(x, self.lower_boundary, self.upper_boundary)
return x
def _permute(self, x): # different from the original paper for simplicity
xx = np.copy(x)
for d in range(self.ndim_problem):
p = self.rng_optimization.choice(self.n_individuals)
xx[:, d] = np.append(xx[p:, d], xx[:p, d])
return xx
def iterate(self, x=None, y=None, args=None):
order, yy, yyy = np.argsort(y), np.empty((2*self._n_parents,)), np.empty((self.n_individuals,))
xx = self._mutate(self._crossover(x[order[:self.n_parents]], y[order[:self.n_parents]]))
for i in range(2*self._n_parents):
if self._check_terminations():
return x, y, np.append(yy, yyy)
yy[i] = self._evaluate_fitness(xx[i], args)
xxx = self._permute(x)
for i in range(self.n_individuals):
if self._check_terminations():
return x, y, np.append(yy, yyy)
yyy[i] = self._evaluate_fitness(xxx[i], args)
x = np.vstack((np.vstack((x[order[:self._n_elitists]], xx)), xxx))
y = np.hstack((np.hstack((y[order[:self._n_elitists]], yy)), yyy))
order = np.argsort(y)[:self.n_individuals] # to keep population size fixed
self._n_generations += 1
return x[order], y[order], np.append(yy, yyy)
def optimize(self, fitness_function=None, args=None):
fitness = CC.optimize(self, fitness_function)
x, y = self.initialize(args)
yy = y # only for printing
while not self._check_terminations():
self._print_verbose_info(fitness, yy)
x, y, yy = self.iterate(x, y, args)
return self._collect(fitness, yy)