The Use of the Evolutionary Algorithm for the Aggregation of Socio-Economic Indices (Considering the Estimate of the Innovative Potential of Russian Regions)

• Autorzy: Daniel Kanevsky , Pavel Chistyakov
• Języki publikacji: EN

Abstrakty

EN

Aggregating indicators are the numerical characteristics of objects and processes, reflecting their global properties, which often defy strict formalization. The problem of calculation of aggregating indicators arises in many branches of social science, economics, and geography. In this paper we introduce a new method, which uses several simple quantitative characteristics to construct a rating aggregating indicator. The evolutionary algorithm, underlying our method, doesn't use a provided formula or function to optimize, thus guaranteeing unbiased results. Moreover, the evolutionary algorithm takes into account modest effects, annihilated by factorial analysis. We illustrate the method calculating the ratings of innovation potential of Russian regions. (original abstract)

Słowa kluczowe

EN

Regional innovation; Algorithms

PL

Innowacyjność regionu; Algorytmy

• Czasopismo: Bulletin of Geography. Socio-economic Series
• Rocznik: 2005
• Numer: No. 4
• Strony: 101--115

Twórcy

autor

Daniel Kanevsky

• Lomonosov Moscov State University, Russia

autor

Pavel Chistyakov

• Lomonosov Moscov State University, Russia

Bibliografia

• Bäck Th. Optimization by Means of Genetic Algorithms, http://citeseer.ist.psu.edu/79660.html
• Bäck Th. and Hoffmeister F. Global Optimization by Means of Evolutionary Algorithms, http://citeseer.ist.psu.edu/219671.html
• Hinterding R., Gielewski H., and Peachey T.C. 1995: The nature of mutation in genetic algorithms. In: Proceedings of the Sixth International Conference on Genetic Algorithms, L.J. Eshelman, ed. San Francisco: Morgan Kaufmann, pp.65-72.
• Holland J.H. 1975: Adaptation in natural and artificial systems, The University of Michigan Press, Ann Arbor.
• Kostenko V.A. 2002: The principles of constructing genetic algorithms and their use in the solving of optimization tasks. The works of the IVth International conference Discrete models in the theory of operating systems, pp. 49-55.
• Redko V.G. 2001: Evolutionary Cybernetics, Moscow, Science.
• Strizjov V.V. 2002: The agreement of expert estimates when constructing aggregating indicators, author's abstract of Ph. D. thesis, Moscow.