RASH: A Self-adaptive Random Search Method

This paper presents an adaptive stochastic search algorithm for the optimization of functions of continuous variables where the only hypothesis is the pointwise computability of the function. The main design criterion of the proposed scheme consists of the adaptation of a search region by an affine transformation which takes into account the local knowledge derived from trial points generated with uniform probability. Heuristic adaptation of the step size and direction allows the largest possible movement per function evaluation. Our results show that the proposed technique is, in spite of its simplicity, a promising building block to consider for the development of more complex optimization algorithms, particularly in those cases where the objective function evaluation is expensive.