Computers thatprogram themselves' has long been an aim of computer scientists. Recently
genetic programming (GP) has started to show its promise by automatically evolving …

After a decade of development, genetic algorithms and genetic programming have become
a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic …

Based on predictions of stock-pricesusing genetic programming (or GP), a possiblyprofitable
trading strategy is proposed. A metricquantifying the probability that a specific timeseries is …

Prediction of non-life insurance companies insolvency has arised as an important problem
in the field of financial research, due to the necessity of protecting the general public whilst …

Based on the standard genetic programming (GP) paradigm, we introduce a new probability
measure of time series' predictability. It is computed as a ratio of two fitness values (SSE) …

A method for the data mining task of data classification, suitable to be implemented on
massively parallel architectures, is proposed. The method combines genetic programming …

Time series analysis has always been an important and interesting research field due to its
frequent appearance in different applications. In the past, many approaches based on …

We show genetic programming (GP) populations can evolve under the influence of a Pareto
multi-objective fitness and program size selection scheme, from “perfect” programs which …

This chapter presents a bibliography on the application of evolutionary computation to
economics and finance. Publications included in this bibliography are classified by …

Stock markets are very important in modern societies and their behaviour have serious
implications in a wide spectrum of the world's population. Investors, governing bodies and …