The random search strategy consists of sampling solutions over the entire search space using a uniform probability distribution. Each future sample is independent of the samples that precede it.
The strategy has minimal time and memory complexity, as it only requires a candidate solution build routine and a candidate solution evaluation routine, both of which can be calibrated using the approach.
The worst performance for locating optima is worse than a search domain enumeration, since the random search has no memory and can perform blind resampling.
Random search can return a reasonable approximation of the optimal solution within a reasonable time frame with low problem dimensionality, although the approach does not scale well to the size of the problem (such as the number of dimensions).
The results can be used as the basis for another search technique, such as a local search technique (such as the Hill Climbing algorithm), which can be used to locate the best solution in the vicinity of the good candidate solution.