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Chaos Powered Grammatical Evolution

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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Submitted on : Wednesday, November 30, 2016 - 11:12:06 AM
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Ivan Zelinka, Petr Šaloun, Roman Senkerik. Chaos Powered Grammatical Evolution. 13th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Nov 2014, Ho Chi Minh City, Vietnam. pp.455-464, ⟨10.1007/978-3-662-45237-0_42⟩. ⟨hal-01405629⟩



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