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- Elena Bautu, Andrei Bautu, Henri Luchian
- Ninth International Symposium on Symbolic and…
- 2007

Many papers focused on fine-tunning the gene expression programming (GEP) operators or their application rates in order to improve the performances of the algorithm. Much less work was done on optimizing the structural parameters of the chromosomes (i.e. number of genes and gene size). This is probably due to the fact that the no free lunch theorem states… (More)

- Elena Bautu, Sun Kim, Andrei Bautu, Henri Luchian, Byoung-Tak Zhang
- IEEE Congress on Evolutionary Computation
- 2009

The paper proposes a hypernetwork-based method for stock market prediction through a binary time series problem. Hypernetworks are a random hypergraph structure of higher-order probabilistic relations of data. The problem we tackle concerns the prediction of price movements (up/down) on stock markets. Compared to previous approaches, the proposed method… (More)

- Andrei Bautu, Elena Bautu
- 2008

Weather systems use extremely complex combinations of mathematical tools for analysis and forecasting. Unfortunately, due to phenomena in the world climate, such as the greenhouse effect, classical models may become inadequate mostly because they lack adaptation. Therefore, the weather prediction problem is suited for heuristic approaches, such as… (More)

- Elena Bautu, Andrei Bautu, Henri Luchian
- Seventh International Symposium on Symbolic and…
- 2005

This paper presents a novel method to perform regression on a finite sample of noisy data. The purpose is to obtain a mathematical model for data which is both reliable and valid, yet the analytical expression is not restricted to any particular form. To obtain a statistical model of the noisy data set we use symbolic regression with pseudorandom number… (More)

Meteorological time series are characterized by important spatial and temporal variation. Model determination and the prediction of evolution of such series is of high importance for different practical purposes, even if discovering evolution patterns in such series is a very difficult problem. In this article we describe an adaptive evolutionary technique… (More)

- Alina Barbulescu, Elena Bautu
- Int. Arab J. Inf. Technol.
- 2012

The problem we tackle concerns forecasting time series in financial markets. AutoRegressive Moving-Average (ARMA) methods and computational intelligence have also been used to tackle this problem. We propose a novel method for time series forecasting based on a hybrid combination of ARMA and Gene Expression Programming (GEP) induced models. Time series from… (More)

- Elena Bautu, Andrei Bautu, Henri Luchian
- Seventh International Symposium on Symbolic and…
- 2005

Evolutionary techniques have been widely accepted as an effective meta-heuristic for a wide variety of problems in different domains. The main purpose of this paper is to present a symbolic technique based on the evolutionary paradigm gene expression programming (GEP) for solving Fredholm first type integral equations. We present the main traits of the gene… (More)

- Andrei Bautu, Elena Bautu
- NICSO
- 2007

This paper presents a method for searching ground states of Ising spin glasses. The Ising model is one of the most commonly used because of its simplicity and its accuracy in representing real problems. We tackle the problem of finding ground states with particle swarm optimization (PSO), a population-based stochastic optimization technique inspired by… (More)

- Andrei Bautu, Elena Bautu, Henri Luchian
- Ninth International Symposium on Symbolic and…
- 2007

This paper compares two particle swarm optimization (PSO) hybrids on the problem of searching ground states of Ising spin glasses. The Ising model is one of the most widely used models for disordered systems in statistical physics. Finding the ground state of an Ising spin glass can be expressed as the problem of determining the minimum weighted cut in a… (More)