About One Method of Building Model Recognition State of Complex Dynamic Systems

Abstract

Evolution has provided a source of inspiration for algorithm designers since the birth of computers. The resulting field, evolutionary computation, has been successful in solving engineering tasks ranging in outlook from molecular to the astronomic. Today, the field is entering a new phase as evolutionary algo-rithms are developed that take place in hardware, opening up new avenues to-wards autonomous machines that can adapt to their environment. In this article given evolutionary algorithms, their main aspects. There are many different var-iants of Evolutionary Algorithms. The common underlying idea behind all these techniques is the same: given a population of individuals the environmental pres-sure causes natural selection (survival of the fittest) and this causes a rise in the fitness of the population. Given a quality function to be maximized we can randomly create a set of candidate solutions, i.e., elements of the function’s domain, and apply the quality function as an abstract fitness measure. Recombination is an operator applied to two or more selected candidates (the so-called parents) and results one or more new candidates (the children). Mutation is applied to one candidate and results in one new candidate. Also, article is devoted to research of the objects described by quantitative attributes. The considered problem is solved within evolutionary calculations.