Areas of supervision
Evolutionary computation, genetic algorithms & genetic programming, neural networks, biocomputation, machine learning, applications to time-series prediction, financial engineering & data mining.
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.
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
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
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.
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.
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.
Publications and research outputs
Nikolaev, Nikolay and Iba, H.. 2006. Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods. Springer. ISBN 0387312390
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
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
Mirikitani, Derrick and Nikolaev, Nikolay. 2011. Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks. Neural Computing and Applications, 20(1), pp. 79-89. ISSN 0941-0643
Nikolaev, Nikolay; Tino, Peter and Smirnov, Evgueni. 2011. Time-Dependent Series Variance Estimation via Recurrent Neural Networks. Artificial Neural Networks and Machine Learning – ICANN 2011, 6791(n/a), pp. 176-184. ISSN 0302-9743
Smirnov, Evgueni; Nikolaev, Nikolay and Nalbantov, Georgi. 2010. Single-Stacking Conformity Approach to Reliable Classification. Artificial Intelligence: Methodology, Systems, and Applications, 6304, pp. 161-170. ISSN 0302-9743
Smirnov, Evgueni; Nalbantov, Georgi and Nikolaev, Nikolay. 2010. k-Version-Space Multi-class Classification Based on k-Consistency Tests. European Conference on Machine Learning and Knowledge Discovery in Databases, 6323, pp. 277-292. ISSN 0302-9743
Mirikitani, D. T. and Nikolaev, Nikolay. 2010. Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling. IEEE Transactions on Neural Networks, 21(2), pp. 262-274. ISSN 1045-9227
Mirikitani, Derrick T. and Nikolaev, Nikolay. 2010. Efficient online recurrent connectionist learning with the ensemble Kalman filter. Neurocomputing, 73(4 - 6), pp. 1024-1030. ISSN 0925-2312
Nikolaev, Nikolay and de Menezes, L. 2008. Sequential Bayesian Kernel Modelling with Non-Gaussian Noise. Neural Networks, 21(1), pp. 36-47. ISSN 0893-6080
Nikolaev, Nikolay. 2003. Polynomial Harmonic GMDH Learning Networks for Time Series Modeling. Neural Networks, 16(10), pp. 1527-1540. ISSN 08936080
Nikolaev, Nikolay. 2003. Learning polynomial feedforward neural networks by genetic programming and backpropagation. IEEE Transactions on Neural Networks, 14(2), pp. 337-350. ISSN 10459227
Nikolaev, Nikolay and Iba, Hitoshi. 2002. Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform. International Journal of Neural Systems, 12(5), pp. 399-410. ISSN 0129-0657
Nikolaev, Nikolay. 2001. Regularization Approach to Inductive Genetic Programming. IEEE Transactions on Evolutionary Computation, 5(4), pp. 359-375. ISSN 1089778X
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.