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PhD in Economics, Associate professor
Dniprodzerzhinsk state technical university
QUESTIONS DEVELOPMENT OF ENTERPRISE ORGANIZATION WITH THE USE OF GENETIC ALGORITHMS
A genetic algorithm of [GA] is a method of optimization, reasonable on conceptions of natural selection and genetics. In this approach removable that characterize decision presented in a look GENE in a chromosome. GA operates the finite set of decisions by the populations of generates new decisions as using different combinations of parts of decisions of population, such operators as selection, recombination of [krosing-over] and mutation. GA allows to decide the tasks of prognostication, classification of search of optimal variants and quite irreplaceable willows these cases, when in the ordinary terms of decision of task reasonable on intuition or experience, but not on strict in mathematical sense of its description.
A requirement in a prognosis and adequate estimation of consequences of carrying out administrative decisions results in the necessity of design of dynamics of replacement of basic parameters of activity of organization, features of its cooperating with an environment, with what an exchange comes true by resources in the conditions of concrete or cooperative mutual relations. For this purpose effective methods and criterions of estimation of adequacy of models of models of development of organization are needed, that is sent not only to maximization of criterions of type ''profit'', ''profitability'', and on optimization of relations of organization with an environment, increase of its competitiveness and firmness.
Realization of procedures of evolutional design comes true on the basis of GA,GA has two defects. Firstly, raising of task dose not give an opportunity to analyses statistical meaning fullness got with their help of decision and secondly, effectively to set forth a task and define a criterion to the selection of chromosomes it is been in strength only to the specialist. By virtue of these factors, GA find small practical application in business and management and examined rather as an instrument of scientific research. In hired given a shoot to refute this statement.
For example, development of enterprise organization can be successfully designed on the basis of GA. With their help it is possible to choose optimal prices on products and services of some organization in the conditions of competition, and with that to entrap more clients and get a more profit. Every [branch] genes of organization [the individual of population] can be appraised by the measure oh its suitability to the changes of internal market environment. The less all suitability individuals of [branches] quite can disappear as a result of evolution.
We will bring a conformable genetic algorithm over [simplified]:
introduction Initial structure of organization [the Initial population];
STRUCTURE [procedure of estimation structure on application]
Feet:=0 [flag for completion of evolutional process] beginning of cycle while [Feet=0]
A SELECTION [is procedure of genetic selection of new generation]
Beginning of cycle while [MEASURE] [cycle of reproduction with the criterion of the MEASURE - measure of efficiency of activity of organization]
PARENTS [procedure of selection of two structures of branches of unite [cross] on new
THE UNITE [procedure of forming of new organization [branch]]
AN ESTIMATION [is procedure of estimation of firmness and competitiveness of new organization]
INCLUSION [procedure include [not included] to the next generation of business organizations]
End of cycle
MUTATION [procedure of evolution [mutation] of new generation]
If [PROCESS] [verification of completion of process of evolution]
By such method, GA is based on three mechanisms; firstly, the selection of capacity individuals-chromosomes corresponds, that the most optimal decision, secondly, on crossing of individuals-development of new individuals by means of mixing of chromosomal sets of the selected individuals; and, in the thirdly, mutations - accidental replacements of genes in some individuals of population. As a result of replacement of generations made decision the put task, that already can not be farther.
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3. Batishchev, D. I. (1995), Genetic algorithms for solving extreme of problems [Genetychni algorytmy vyrishennya ekstremal’nykh zadach], Novdorod university, N. Novdorod, 62 p. (rus)
4. Stepanov, V. N., Stepanova, E.V. (2013), Martix-incidence analysis of the socio-economic and ecological processes (theoretical, methodological and applied bases) [Matrichno-intsidentnostnyy analiz sotsialno-ekonomiko-ekologicheskikh protsessov (teoretiko-metodologicheskie I prikladnye osnovy)], IMPEER NAS of Ukraine, Odessa, 170 p. (rus)
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