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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Keywords
- Particle Swarm Optimization
- Evolutionary Algorithm
- Chaotic System
- Symbolic Regression
- Evolutionary Strategy
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Zelinka, I., Šaloun, P., Senkerik, R. (2014). Chaos Powered Grammatical Evolution. In: Saeed, K., Snášel, V. (eds) Computer Information Systems and Industrial Management. CISIM 2015. Lecture Notes in Computer Science, vol 8838. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-45237-0_42
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DOI: https://doi.org/10.1007/978-3-662-45237-0_42
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