# Dr Nikolay Nikolaev

## Staff details

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.

## 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.

## Publications

#### Book

** Adaptive Learning of Polynomial Networks: Genetic Programming, Backpropagation and Bayesian Methods **

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

#### Book Section

** Nonlinear filtering of asymmetric stochastic volatility models and Value-at-Risk estimation **

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

** Time-dependent series variance learning with recurrent mixture density networks **

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

** Heavy-tailed mixture GARCH volatility modeling and Value-at-Risk estimation **

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

** Time-Dependent Series Variance Estimation via Recurrent Neural Networks **

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

** Nonlinear maximum likelihood estimation of electricity spot prices using recurrent neural networks **

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

** Single-Stacking Conformity Approach to Reliable Classification **

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

** Recursive Bayesian Recurrent Neural Networks for Time-Series Modeling **

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

** Efficient online recurrent connectionist learning with the ensemble Kalman filter **

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

** k-Version-Space Multi-class Classification Based on k-Consistency Tests **

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

** Sequential Bayesian Kernel Modelling with Non-Gaussian Noise **

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

** Polynomial Harmonic GMDH Learning Networks for Time Series Modeling **

Nikolaev, Nikolay. 2003. Polynomial Harmonic GMDH Learning Networks for Time Series Modeling. Neural Networks, 16(10), pp. 1527-1540. ISSN 08936080

** Learning polynomial feedforward neural networks by genetic programming and backpropagation **

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

** Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform **

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

** Accelerated Genetic Programming of Polynomials **

Nikolaev, Nikolay and Iba, Hitoshi. 2001. Accelerated Genetic Programming of Polynomials. Genetic Programmimg and Evolvable Machines, 2(3), pp. 231-257. ISSN 1389-2576

** Regularization Approach to Inductive Genetic Programming **

Nikolaev, Nikolay. 2001. Regularization Approach to Inductive Genetic Programming. IEEE Transactions on Evolutionary Computation, 5(4), pp. 359-375. ISSN 1089778X