Simple Random Search (SRS)
- class pypop7.optimizers.rs.srs.SRS(problem, options)[source]
Simple Random Search (SRS).
Note
SRS is an adaptive random search method, originally designed by Rosenstein and Barto for direct policy search in reinforcement learning. Since it uses a simple individual-based random sampling strategy, it easily suffers from a limited exploration ability for large-scale black-box optimization (LSBBO). 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.
- 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 (float),
’x’ - 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’].
’alpha’ - factor of global step-size (float, default: 0.3),
’beta’ - adjustment probability for exploration-exploitation trade-off (float, default: 0.0),
’gamma’ - factor of search decay (float, default: 0.99),
’min_sigma’ - minimum of global step-size (float, default: 0.01).
Examples
Use the 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.srs import SRS 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... 'x': 3*numpy.ones((2,)), 11... 'sigma': 0.1} 12>>> srs = SRS(problem, options) # initialize the optimizer class 13>>> results = srs.optimize() # run the optimization process 14>>> # return the number of used function evaluations and found best-so-far fitness 15>>> print(f"SRS: {results['n_function_evaluations']}, {results['best_so_far_y']}") 16SRS: 5000, 0.0017821578376762473
For its correctness checking of coding, the code-based repeatability report cannot be provided owing to the lack of its simulation environment in the original paper. Instead, we used the comparison-based strategy to validate its correctness as much as possible (though there still has a risk to be wrong).
- alpha
factor of global step-size.
- Type:
float
- beta
adjustment probability for exploration-exploitation trade-off.
- Type:
float
- gamma
factor of search decay.
- Type:
float
- min_sigma
minimum of global step-size.
- Type:
float
- sigma
final global step-size (updated during optimization).
- Type:
float
- x
initial (starting) point.
- Type:
array_like
References
Rosenstein, M.T. and Grupen, R.A., 2002, May. Velocity-dependent dynamic manipulability. In Proceedings of IEEE International Conference on Robotics and Automation (pp. 2424-2429). IEEE. https://ieeexplore.ieee.org/abstract/document/1013595
Rosenstein, M.T. and Barto, A.G., 2001, August. Robot weightlifting by direct policy search. In International Joint Conference on Artificial Intelligence (pp. 839-846). https://dl.acm.org/doi/abs/10.5555/1642194.1642206