Pure Random Search (PRS)¶
- class pypop7.optimizers.rs.prs.PRS(problem, options)¶
Pure Random Search (PRS).
Note
PRS is one of the simplest and earliest black-box optimizers, dating back to at least 1950s. Although recently it has been successfully applied in several relatively low-dimensional problems (particularly hyper-parameter optimization), it generally suffers from the famous curse of dimensionality for large-scale black-box optimization (LSBBO), owing to the lack of adaptation, a highly desirable property for most sophisticated search algorithms. Therefore, it is highly recommended to first attempt more advanced (e.g. population-based) methods for LSBBO.
Here we include it mainly for benchmarking purpose. As pointed out in Probabilistic Machine Learning, “this should always be tried as a baseline”.
- 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).
Examples
Use the PRS optimizer 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.rs.prs import PRS 4>>> problem = {'fitness_function': rosenbrock, # define problem arguments 5... 'ndim_problem': 2, 6... 'lower_boundary': -5*numpy.ones((2,)), 7... 'upper_boundary': 5*numpy.ones((2,))} 8>>> options = {'max_function_evaluations': 5000, # set optimizer options 9... 'seed_rng': 2022} 10>>> prs = PRS(problem, options) # initialize the optimizer class 11>>> results = prs.optimize() # run the optimization process 12>>> # return the number of used function evaluations and found best-so-far fitness 13>>> print(f"PRS: {results['n_function_evaluations']}, {results['best_so_far_y']}") 14PRS: 5000, 0.11497678820610932
For its correctness checking of coding, refer to this code-based repeatability report for more details.
References
Bergstra, J. and Bengio, Y., 2012. Random search for hyper-parameter optimization. Journal of Machine Learning Research, 13(2). https://www.jmlr.org/papers/v13/bergstra12a.html
Schmidhuber, J., Hochreiter, S. and Bengio, Y., 2001. Evaluating benchmark problems by random guessing. A Field Guide to Dynamical Recurrent Networks, pp.231-235. https://ml.jku.at/publications/older/ch9.pdf
Brooks, S.H., 1958. A discussion of random methods for seeking maxima. Operations Research, 6(2), pp.244-251. https://pubsonline.informs.org/doi/abs/10.1287/opre.6.2.244