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.

  • 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).


initial (starting) point.




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