Chaos powered grammatical evolution

In this paper we discuss alternative nonrandom generators for symbolic regression algorithms and compare its variants powered by classical pseudo-random number generator and chaotic systems. Experimental data from previous experiments reported for genetic programming and analytical programming is used. The selected algorithms are differential evolution and SOMA. Particle swarm, simulated annealing and evolutionary strategies are in process of investigation. All of them are mutually used in scheme Master-Slave meta-evolution for final complex structure fitting and its parameter estimation.Agency of the Czech Republic-GACR [P103/13/08195S]; SGS [SP2014/159]; VSB-Technical University of Ostrava, Czech Republic; European Regional Development Fund [CZ.1.05/2.1.00/03.0089