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fcmaes.py
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import numpy as np # engine for numerical computing
from pypop7.optimizers.es.es import ES
class FCMAES(ES):
"""Fast Covariance Matrix Adaptation Evolution Strategy (FCMAES).
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`),
* 'mean' - initial (starting) point, aka mean of Gaussian search distribution (`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']`.
* 'n_individuals' - number of offspring, aka offspring population size (`int`, default:
`4 + int(3*np.log(problem['ndim_problem']))`),
* 'n_parents' - number of parents, aka parental population size (`int`, default:
`int(options['n_individuals']/2)`).
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 # engine for numerical computing
>>> from pypop7.benchmarks.base_functions import rosenbrock # function to be minimized
>>> from pypop7.optimizers.es.fcmaes import FCMAES
>>> 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,
... 'mean': 3*numpy.ones((2,)),
... 'sigma': 0.1} # the global step-size may need to be tuned for better performance
>>> fcmaes = FCMAES(problem, options) # initialize the optimizer class
>>> results = fcmaes.optimize() # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"FCMAES: {results['n_function_evaluations']}, {results['best_so_far_y']}")
FCMAES: 5000, 0.016679956606138215
For its correctness checking of coding, refer to `this code-based repeatability report
<https://tinyurl.com/3hmkaymn>`_ for more details.
Attributes
----------
mean : `array_like`
initial (starting) point, aka mean of Gaussian search distribution.
n_individuals : `int`
number of offspring, aka offspring population size.
n_parents : `int`
number of parents, aka parental population size.
sigma : `float`
final global step-size, aka mutation strength.
References
----------
Li, Z., Zhang, Q., Lin, X. and Zhen, H.L., 2020.
Fast covariance matrix adaptation for large-scale black-box optimization.
IEEE Transactions on Cybernetics, 50(5), pp.2073-2083.
https://ieeexplore.ieee.org/abstract/document/8533604
Li, Z. and Zhang, Q., 2016.
What does the evolution path learn in CMA-ES?.
In Parallel Problem Solving from Nature (pp. 751-760).
Springer International Publishing.
https://link.springer.com/chapter/10.1007/978-3-319-45823-6_70
"""
def __init__(self, problem, options):
ES.__init__(self, problem, options)
self.m = self.n_individuals # number of evolution paths
self.c = 2.0/(self.ndim_problem + 5.0) * options.get('c_multi') # learning rate of evolution path update
self.c_1 = 1.0/(3.0*np.sqrt(self.ndim_problem) + 5.0) # sampling factor
self.c_s = 0.3 # learning rate of rank-based success rule for global step-size adaptation
self.q_star = 0.27 # target of rank-based success rule for global step-size adaptation
self.d_s = 1.0 # damping factor of rank-based success rule for global step-size adaptation
self.n_steps = self.ndim_problem # updating frequency of direction vector set
self._x_1 = 1.0 - self.c_1
self._x_2 = np.sqrt((1.0 - self.c_1)*self.c_1)
self._x_3 = np.sqrt(self.c_1)
self._p_1 = 1.0 - self.c
self._p_2 = None
self._rr = None
# self.metabbo_config = options['config']
def initialize(self, is_restart=False):
mean = self._initialize_mean(is_restart) # mean of Gaussian search distribution
x = np.empty((self.n_individuals, self.ndim_problem)) # offspring population
y = np.empty((self.n_individuals,)) # fitness (no evaluation)
p = np.zeros((self.ndim_problem,)) # evolution path
p_hat = np.zeros((self.m, self.ndim_problem)) # direction vector set
s = 0
self._p_2 = np.sqrt(self.c*(2.0 - self.c)*self._mu_eff)
self._rr = np.arange(self.n_parents*2) + 1
return mean, x, y, p, p_hat, s
# def iterate(self, mean=None, x=None, y=None, p=None, p_hat=None, args=None):
# for i in range(self.n_individuals):
# if self._check_terminations():
# return x, y
# z = self.rng_optimization.standard_normal((self.ndim_problem,))
# if self._n_generations < self.m: # unbiased sampling when starting
# x[i] = mean + self.sigma*z
# else:
# x[i] = mean + self.sigma*(self._x_1*z +
# self._x_2*self.rng_optimization.standard_normal()*p_hat[i] +
# self._x_3*self.rng_optimization.standard_normal()*p)
# y[i] = self._evaluate_fitness(x[i], args)
# return x, y
def iterate(self, mean=None, x=None, y=None, p=None, p_hat=None, args=None):
for i in range(self.n_individuals):
if self._check_terminations():
return x, y
z = self.rng_optimization.standard_normal((self.ndim_problem,))
if self._n_generations < self.m: # unbiased sampling when starting
x[i] = mean + self.sigma * z
else:
x[i] = mean + self.sigma * (self._x_1 * z +
self._x_2 * self.rng_optimization.standard_normal() * p_hat[i] +
self._x_3 * self.rng_optimization.standard_normal() * p)
y = self._evaluate_fitness(x, args)
return x,y
def _update_distribution(self, mean=None, x=None, y=None, p=None, p_hat=None, s=None, y_bak=None):
order = np.argsort(y)[:self.n_parents]
y.sort()
mean_bak = np.dot(self._w[:self.n_parents], x[order])
p = self._p_1*p + self._p_2*(mean_bak - mean)/self.sigma
if self._n_generations % self.n_steps == 0:
p_hat[:-1] = p_hat[1:]
p_hat[-1] = p
if self._n_generations > 0:
r = np.argsort(np.hstack((y_bak[:self.n_parents], y[:self.n_parents])))
rr = self._rr[r < self.n_parents] - self._rr[r >= self.n_parents]
q = np.dot(self._w, rr)/self.n_parents
s = (1.0 - self.c_s)*s + self.c_s*(q - self.q_star)
self.sigma *= np.exp(s/self.d_s)
self._n_generations += 1
return mean_bak, p, p_hat, s
def restart_reinitialize(self, mean=None, x=None, y=None, p=None, p_hat=None, s=None):
if self.is_restart and ES.restart_reinitialize(self, y):
self.d_s *= 2.0
self.m = self.n_individuals
mean, x, y, p, p_hat, s = self.initialize(True)
return mean, x, y, p, p_hat, s
def optimize(self, fitness_function=None, args=None):
fitness = ES.optimize(self, fitness_function)
mean, x, y, p, p_hat, s = self.initialize()
while not self.termination_signal:
y_bak = np.copy(y)
x, y = self.iterate(mean, x, y, p, p_hat, args)
if self._check_terminations():
break
self._print_verbose_info(fitness, y)
mean, p, p_hat, s = self._update_distribution(mean, x, y, p, p_hat, s, y_bak)
mean, x, y, p, p_hat, s = self.restart_reinitialize(mean, x, y, p, p_hat, s)
results = self._collect(fitness, y, mean)
results['p'] = p
results['s'] = s
return results