RGP is genetic programming system based on, as well as fully integrated into, the R environment. The system implements classical tree-based genetic programming as well as other variants including, for example, strongly typed genetic programming and Pareto genetic programming. It strives for high modularity through a consistent architecture that allows the customization and replacement of every ...

The use of intelligent systems for stock market predictions has been widely established. This chapter introduces two Genetic Programming (GP) techniques: Multi-Expression Programming (MEP) and Linear Genetic Programming (LGP) for the prediction of two stock indices. The performance is then compared with an artificial neural network trained using Levenberg-Marquardt algorithm and Takagi-Sugeno n...

Non-linear Iterated Function Systems (IFSs) are very powerful mathematical objects related to fractal theory, that can be used in order to generate (or model) very irregular shapes. We investigate in this paper how Genetic Programming techniques can be efficiently exploited in order to generate randomly or interactively artistic “fractal” 2D shapes. Two applications are presented for different ...

<p style='text-indent:20px;'>In this paper, a fuzzy linear fractional set covering problem is solved. The non-linearity of the objective function as well its fuzziness make it difficult and complex to be solved effectively. To overcome these difficulties, using concepts theory component-wise optimization, converted crisp multi-objective non-linear problem. In order tackle obtained problem...

AbstractUnderstanding operator bias in evolutionary computation is important because it is possible for the operator’s biases to work against the intended biases induced by the fitness function. In recent work we showed how developments in GP schema theory can be used to better understand the biases induced by the standard subtree crossover when genetic programming is applied to variable length...