Optimization is a way of solving uncertainties by multichoice analysis. On the other side, uncertainty is a scientific category and an unavoidable attribute of human life as such.

Optimization problems are aimed at improving a state of a certain system, like improve quality, maximize revenues, increase speed, flight range, resource efficiency, etc.

There are many optimization methods, each of them is suitable for a certain type of problems and has its own advantages and disadvantages. It may seem incorrect to claim the development of a truly general-purpose optimization method; nevertheless we do it. Using the genetic approach and elements of random search we to some extent have solved the problem of incompatibility between the local and the global for large dimensionality uncertain problems. Within a single algorithm we have achieved high accuracy, convergence, solution stability for continuous, integer, discrete and mixed problems.

(genetic algorithms are new approach to modeling based on the evolution theory in biology and uses the concepts similar to population, descendant, parent individuals, mutations, chromosomes, genes, etc.)