Search Gradient-based Evolution Strategy (SGES)
- class pypop7.optimizers.nes.sges.SGES(problem, options)[source]
Search Gradient-based Evolution Strategy (SGES).
Note
Here we include SGES (also called vanilla version of NES) only for theoretical and educational purposes, since in practice advanced versions (e.g., ENES, XNES, SNES, and R1NES) are more preferred than SGES in most cases.
- 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’].
’lr_mean’ - learning rate of distribution mean update (float, default: 0.01),
’lr_sigma’ - learning rate of global step-size adaptation (float, default: 0.01).
Examples
Use the black-box optimizer SGES to minimize the well-known test function Rosenbrock:
1>>> import numpy 2>>> from pypop7.benchmarks.base_functions import rosenbrock # function to be minimized 3>>> from pypop7.optimizers.nes.sges import SGES 4>>> problem = {'fitness_function': rosenbrock, # to define problem arguments 5... 'ndim_problem': 2, 6... 'lower_boundary': -5.0*numpy.ones((2,)), 7... 'upper_boundary': 5.0*numpy.ones((2,))} 8>>> options = {'max_function_evaluations': 5000, # to set optimizer options 9... 'seed_rng': 2022, 10... 'mean': 3.0*numpy.ones((2,))} 11>>> sges = SGES(problem, options) # to initialize the optimizer class 12>>> results = sges.optimize() # to run the optimization process 13>>> print(f"SGES: {results['n_function_evaluations']}, {results['best_so_far_y']}") 14SGES: 5000, 0.0190
- lr_mean
learning rate of distribution mean update (should > 0.0).
- Type:
float
- lr_sigma
learning rate of global step-size adaptation (should > 0.0).
- Type:
float
- mean
initial (starting) point, aka mean of Gaussian search/sampling/mutation distribution. 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’], by default.
- Type:
array_like
- n_individuals
number of offspring/descendants, aka offspring population size (should > 0).
- Type:
int
- n_parents
number of parents/ancestors, aka parental population size (should > 0).
- Type:
int
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.
Schaul, T., 2011. Studies in continuous black-box optimization. Doctoral Dissertation, Technische Universität München.
Please refer to the official Python source code from PyBrain (now not actively maintained): https://github.com/pybrain/pybrain/blob/master/pybrain/optimization/distributionbased/ves.py