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.

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.