Goldsmiths - University of London

Dr Nikolay Nikolaev

Papers presented

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.

Selected publications

Journal publications:

Nikolaev,N. and Iba,H. (2003). Polynomial Harmonic GMDH Learning Networks for Time Series Modeling, Neural Networks, vol.16, N:7 (in press).

Nikolaev,N. and Iba,H. (2003). Learning Polynomial Feedforward Neural Networks by Inductive Genetic Programming and Backpropagation,
IEEE Transactions on Neural Networks, vol.14, N:2, pp.337-350.

Nikolaev,N. and Iba,H. (2002). Genetic Programming of Polynomial Harmonic Networks using the Discrete Fourier Transform, International
Journal of Neural Systems, vol.12, N:5, pp.399-410.

Nikolaev,N. and Iba,H. (2001). Accelerated Genetic Programming of Polynomials, Genetic Programmimg and Evolvable Machines, Kluwer
Academic Publ., vol.2, N:3, pp.231-257.

Nikolaev,N. and Iba,H. (2001). Regularization Approach to Inductive Genetic Programming, IEEE Transactions on Evolutionary Computation,
vol.5, N:4, pp.359-375.