import numpy as np
from pypop7.optimizers.ga.ga import GA
[docs]class GENITOR(GA):
"""GENetic ImplemenTOR (GENITOR).
.. note:: `"Selective pressure and population diversity should be controlled as directly as possible."---[Whitley,
1989] <https://dl.acm.org/doi/10.5555/93126.93169>`_
This is a *slightly modified* version of `GENITOR` for continuous optimization. Originally `GENITOR` was proposed
to solve challenging `neuroevolution <https://www.nature.com/articles/s42256-018-0006-z>`_ problems by Whitley,
`the recipient of IEEE Evolutionary Computation Pioneer Award 2022 <https://tinyurl.com/456as566>`_.
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 setting (`key`):
* 'n_individuals' - population size (`int`, default: `100`),
* 'cv_prob' - crossover probability (`float`, default: `0.5`).
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.ga.genitor import GENITOR
>>> 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}
>>> genitor = GENITOR(problem, options) # initialize the optimizer class
>>> results = genitor.optimize() # run the optimization process
>>> # return the number of function evaluations and best-so-far fitness
>>> print(f"GENITOR: {results['n_function_evaluations']}, {results['best_so_far_y']}")
GENITOR: 5000, 0.004382445279905116
For its correctness checking of coding, the code-based repeatability report cannot be provided owing to
the lack of its simulation environment.
Attributes
----------
cv_prob : `float`
crossover probability.
n_individuals : `int`
population size.
References
----------
https://www.cs.colostate.edu/~genitor/
Whitley, D., Dominic, S., Das, R. and Anderson, C.W., 1993.
Genetic reinforcement learning for neurocontrol problems.
Machine Learning, 13, pp.259-284.
https://link.springer.com/article/10.1023/A:1022674030396
Whitley, D., 1989, December.
The GENITOR algorithm and selection pressure: Why rank-based allocation of reproductive trials is best.
In Proceedings of International Conference on Genetic Algorithms (pp. 116-121).
https://dl.acm.org/doi/10.5555/93126.93169
"""
def __init__(self, problem, options):
GA.__init__(self, problem, options)
self.cv_prob = options.get('cv_prob', 0.5) # crossover probability
assert 0.0 <= self.cv_prob <= 1.0
_rank_prob = np.arange(self.n_individuals, 0, -1) - 1.0
self.rank_prob = _rank_prob/np.sum(_rank_prob)
def initialize(self, args=None):
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
cv_probs = self.cv_prob*np.ones((self.n_individuals,))
for i in range(self.n_individuals):
if self._check_terminations():
break
y[i] = self._evaluate_fitness(x[i], args)
return x, y, cv_probs
def iterate(self, x=None, y=None, cv_probs=None, args=None):
order, xx, yy = np.argsort(y), None, None
# use rank-based selection for two parents
offspring = self.rng_optimization.choice(order, size=2, replace=False, p=self.rank_prob)
if self.rng_optimization.random() < cv_probs[offspring[0]]: # crossover
# use intermediate crossover (not one-point crossover proposed in the original paper)
xx = (x[offspring[0]] + x[offspring[1]])/2.0
yy = self._evaluate_fitness(xx, args)
if yy < y[order[-1]]: # to replace the worst individual
x[order[-1]], y[order[-1]] = xx, yy
cv_probs[offspring[0]] += 0.1
else:
cv_probs[offspring[0]] -= 0.1
cv_probs[offspring[0]] = np.maximum(0.05, np.minimum(0.95, cv_probs[offspring[0]]))
else: # mutation
xx = np.copy(x[offspring[0]]) # offspring
xx += self.rng_optimization.uniform(self.lower_boundary, self.upper_boundary)/10.0
yy = self._evaluate_fitness(xx, args)
if yy < y[order[-1]]: # to replace the worst individual
x[order[-1]], y[order[-1]] = xx, yy
self._n_generations += 1
return x, yy, cv_probs
def optimize(self, fitness_function=None, args=None):
fitness = GA.optimize(self, fitness_function)
x, y, cv_probs = self.initialize(args)
yy = y # only for printing
while not self._check_terminations():
self._print_verbose_info(fitness, yy)
x, yy, cv_probs = self.iterate(x, y, cv_probs, args)
return self._collect(fitness, yy)