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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)
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)
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)
— 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)
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)
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)
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)
Forecasting applications on the stock market attract much interest from researchers in the artificial intelligence field. The problem tackled in this study concerns predicting the direction of change of stock price indices, formulated in terms of binary classification. We use gene expression programming to evolve pools of binary classifiers and investigate(More)