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.
The Traveling Salesman Problem (TSP) is an NP-Complete problem. Many techniques were developed to solve such problem, including Genetic Algorithms (GA's). The goal of this research is to enhance the performance of the Genetic Algorithm (GA) in solving the TSP. We achieve this goal by developing a local search algorithm called Search for Nearest Solution Algorithm (SNSA). This algorithm produces better solutions in acceptable periods of time. A new Crossover operator is proposed and used in the SNSA. Results for benchmark cases of the TSP show that the algorithm can find known optimum solutions. Comparisons between the proposed algorithm and other GA based methods with known crossover techniques show that SNSA has good quality solutions.