Dr Nikolay Nikolaev

Full information in Nikolay's homepage

Areas of supervision

Evolutionary computation, genetic algorithms & genetic programming, neural networks, biocomputation, machine learning, applications to time-series prediction, financial engineering & data mining.

Featured works

Book chapters and conference papers

Nikolaev,N., and Iba,H. (2002). Genetic Programming of Polynomial Models for Financial Forecasting. In: Shu-Heng Chen (Ed.), Genetic
Algorithms and Genetic Programming in Computational Finance, Chapter 5, Kluwer Academic Publ., Boston, MA, pp.103-123.

Nikolaev,N., de Menezes,L. and Iba, H. (2002). Overfitting Avoidance in Genetic Programming of Polynomials, In: Proc. 2002 Congress on
Evolutionary Computation, CEC2002, IEEE Press, Piscataway, NJ, pp.1209-1214.

Nikolaev,N. and Iba, H. (2001). Genetic Programming using Chebishev Polynomials, In: L.Spector, E.D.Goodman, A.Wu, W.B.Langdon,
H.-M.Voigt, M.Gen, S.Sen, M.Dorigo, S.Pezeshk, M.H.Garzon, and E.Burke (Eds.), Proc. of the Genetic and Evolutionary Computation
Conference, GECCO-2001, Morgan Kaufmann Publ., San Francisco, CA, pp.89-96.

Publications and research outputs

Book

Nikolaev, Nikolay and Iba, H.. 2006. Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods. Springer. ISBN 0387312390

Book Section

Nikolaev, Nikolay; De Menezes, L. M. and Smirnov, E.. 2014. Nonlinear filtering of asymmetric stochastic volatility models and Value-at-Risk estimation. In: R. J. Almeida; D. Maringer; V. Palade and A. Serguieva, eds. IEEE/IAFE Conference on Computational Intelligence for Financial Engineering, Proceedings (CIFEr). IEEE, pp. 310-317. ISBN 978-147992380-9

Article

Nikolaev, Nikolay; Smirnov, Evgueni; Stamate, Daniel and Zimmer, Robert. 2019. A Regime-Switching Recurrent Neural Network Model Applied to Wind Time Series. Applied Soft Computing, 80, pp. 723-734. ISSN 1568-4946

Nikolaev, Nikolay; Peter, Tino and Evgueni, Smirnov. 2013. Time-dependent series variance learning with recurrent mixture density networks. Neurocomputing, 122, pp. 501-512. ISSN 0925-2312

Nikolaev, Nikolay; Boshnakov, Georgi N. and Zimmer, Robert. 2013. Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation. Expert Systems with Applications, 40(6), pp. 2233-2243. ISSN 0957-4174

Conference or Workshop Item

Vanegdom, A.; Nikolaev, N. and Garagnani, M.. 2022. 'Standard feedforward neural networks with backprop cannot support cognitive superposition'. In: Bernstein Conference 2022. Berlin, Germany 13-16 September 2022.

Research Interests

Neural networks
statistical learning networks, basis-function networks, constructive learning of the topology and initial weights of
multilayer neural networks; financial engineering by basis-function neural networks; chaotic time-series prediction by
statistical networks.

Genetic Algorithms
Structured genetic algorithms with cooperative subpopulations flowing on fitness sublandscapes; Fourier expansions
of fitness landscapes over regular graphs, messy genetic algorithms for applied economic regression tasks.

Inductive Genetic Programming (iGP):
Evolutionary induction of multivariate high-order polynomials, genetic programming of statistical learning networks,
genetic programming of polynomial discriminant classifiers, regularization in iGP, finite-state automata induction by
iGP.
Data mining
A utomated discovery of polynomials from data with numerical and continuous features; sequential forward and
backward feature selection for construction of multi-layer neural networks.

Machine Learning
Decision tree classifiers, stochastic complexity (Minimum Description Length-MDL) measures for decision tree
learners, multivariate splitting methods for non-linear decision trees; linear and oblique decision trees,
distance-based decision trees.

Current research

My recent work is devoted to genetic programming of tree-structured polynomials, known as statistical learning networks of the
GMDH type. This includes design of stochastic complexity (Minimum Description Length-MDL) and statistical
fitness functions for efficient search navigation. These functions are elaborated using ideas from the
regularization theory aiming at evolution of parsimonious, accurate and predictive polynomials.