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Chaos powered grammatical evolution

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (2014) 8838 455-464
DOI: 10.1007/978-3-662-45237-0_42
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Abstract

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.

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APA

Zelinka, I., Šaloun, P., & Senkerik, R. (2014). Chaos powered grammatical evolution. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8838, pp. 455–464). Springer Verlag. https://doi.org/10.1007/978-3-662-45237-0_42

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