Random Search (RS)¶
- class pypop7.optimizers.rs.rs.RS(problem, options)¶
Random (stochastic) Search (optimization) (RS).
This is the abstract class for all RS classes. Please use any of its instantiated subclasses to optimize the black-box problem at hand. Recently, all of its state-of-the-art versions adopt the population-based random sampling strategy for better exploration in the complex search space.
- 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):
’x’ - initial (starting) point (array_like).
- x¶
initial (starting) point.
- Type:
array_like
References
Gao, K. and Sener, O., 2022, June. Generalizing Gaussian smoothing for random search. In International Conference on Machine Learning (pp. 7077-7101). PMLR. https://proceedings.mlr.press/v162/gao22f.html
Nesterov, Y. and Spokoiny, V., 2017. Random gradient-free minimization of convex functions. Foundations of Computational Mathematics, 17(2), pp.527-566. https://link.springer.com/article/10.1007/s10208-015-9296-2
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
Appel, M.J., Labarre, R. and Radulovic, D., 2004. On accelerated random search. SIAM Journal on Optimization, 14(3), pp.708-731. https://epubs.siam.org/doi/abs/10.1137/S105262340240063X
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
Schmidhuber, J. and Hochreiter, S., 1996. Guessing can outperform many long time lag algorithms. Technical Report. https://www.bioinf.jku.at/publications/older/3204.pdf
Rastrigin, L.A., 1986. Random search as a method for optimization and adaptation. In Stochastic Optimization. https://link.springer.com/chapter/10.1007/BFb0007129
Solis, F.J. and Wets, R.J.B., 1981. Minimization by random search techniques. Mathematics of Operations Research, 6(1), pp.19-30. https://pubsonline.informs.org/doi/abs/10.1287/moor.6.1.19
Schrack, G. and Choit, M., 1976. Optimized relative step size random searches. Mathematical Programming, 10(1), pp.230-244. https://link.springer.com/article/10.1007/BF01580669
Schumer, M.A. and Steiglitz, K., 1968. Adaptive step size random search. IEEE Transactions on Automatic Control, 13(3), pp.270-276. https://ieeexplore.ieee.org/abstract/document/1098903
Matyas, J., 1965. Random optimization. Automation and Remote control, 26(2), pp.246-253. https://tinyurl.com/25339c4x (Since it was written originally in Russian, we cannot read it. However, owing to its historical position, we still choose to include it here, which causes a nonstandard citation.)
Rastrigin, L.A., 1963. The convergence of the random search method in the extremal control of a many parameter system. Automaton & Remote Control, 24, pp.1337-1342. https://tinyurl.com/djfdnpx4
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