Informatica

This is a survey of the main achievements in the methodology and theory of stochastic global optimization. It comprises two complimentary directions: global random search and the methodology based on the use of stochastic models about the objective function. The main attention is paid to theoretically substantiated methods and mathematical results proven in the last 25 years.

The aim of this paper is to explore some features of the functional test generation problem, and on the basis of the gained experience, to propose a practical method for functional test generation. In the paper presented analysis of random search methods and adjacent stimuli generation allowed formulating a practical method for generating functional tests. This method incorporates the analyzed termination conditions of generation, exploits the advantages of random and deterministic search, as well as the feature that the sets of the selected input stimuli can be merged easily in order to obtain a better set of test patterns.

In the present paper, a method of global optimisation (structure adapted search) is proposed. It uses the grid of trial points which are more uniformly distributed for the projections on variables or their groups that make more influence.

The maximization problem for an objective function f given on a feasible region X is considered, where X is a compact subset of Rⁿ and f belongs to a set of continuous multiextremal functions on X can be evaluated at any point x in X without error, and its maximum M=max x∈Xf(x) together with a maximizer x^*(a point x^* in X such that M=f(x^*)) are to be approximated. We consider a class of the global random search methods, underlying an apparatus of the mathematical statistics and generalizing the so-called branch and bound methods.