This paper discusses features of multilayered evolutionary system suitable to identify various systems including their model symbolic regression. Improved sensitivity allows modeling of difficult systems as deterministic chaos ones. The presented paper starts with a brief introduction to previous works and ideas which allowed to build the presented two abstraction levels system. Then the structure of Genetic Programming Algorithm - Evolutionary Strategy hybrid system is described and analyzed, including such problems as suitability to parallel implementation, optimal set of building blocks, or initial population generating rules. GPA-ES system combines GPA to model development with ES used for model parameter estimation and optimization. Such a hybrid system eliminates many weaknesses of standard GPA. The paper concludes with examples of GPA-ES application to Lorenz and Rősler systems regression and suggests application to Neural Network Model design.
Constant evaluation is a key problem for symbolic regression, one solved by means of genetic programming. For constant evaluation, other evolutionary methods are often used. Typical examples are some variants of genetic programming or evolutionary systems, all of which are stochastic. The article compares these methods with a deterministic approach using exponentiated gradient descent. All the methods were tested on single sample function to maintain the same conditions and results are presented in graphs. Finally, three different tasks (ten times each) are compared to check the reliability of the methods tested in the article.
This paper focuses on a two-layer approach to genetic programming algorithm and the improvement of the training process using ensemble learning. Inspired by the performance leap of deep neural networks, the idea of a multilayered approach to genetic programming is proposed to start with two-layered genetic programming. The goal of the paper was to design and implement a twolayer genetic programming algorithm, test its behaviour in the context of symbolic regression on several basic test cases, to reveal the potential to improve the learning process of genetic programming and increase the accuracy of the resulting models. The algorithm works in two layers. In the first layer, it searches for appropriate sub-models describing each segment of the data. In the second layer, it searches for the final model as a non-linear combination of these sub-models. Two-layer genetic programming coupled with ensemble learning techniques on the experiments performed showed the potential for improving the performance of genetic programming.