About: Specific Behaviour of GPA-ES Evolutionary System Observed in Deterministic Chaos Regression     Goto   Sponge   NotDistinct   Permalink

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  • The paper deals with symbolic regression of deterministic chaos systems using GPA-ES system. Lorenz attractor [1], Rősler attractor [2], Rabino- vich-Fabrikant equations [3] and Van der Pol oscillator [4] are used as examples of deterministic chaos systems to demonstrate significant differences in efficiency of symbolic regression of systems described by equations of similar complexity. Within the paper, the source of this behaviour is identified in presence of struc- tures which are hard to be discovered during evolutionary process due to low probability of their occurrence in initial population and by low chance to produce them by standard evolutionary operators given by low probability to form them in single step and low fitness function magnitudes of inter-steps when GPA tries to form them in more steps. This low magnitude of fitness function for particular so- lutions tends to elimination of them thus increases number of needed evolutionary steps. The results are significant for prediction of unknown system states on the base of its model symbolic regression on the base of its previous states.
  • The paper deals with symbolic regression of deterministic chaos systems using GPA-ES system. Lorenz attractor [1], Rősler attractor [2], Rabino- vich-Fabrikant equations [3] and Van der Pol oscillator [4] are used as examples of deterministic chaos systems to demonstrate significant differences in efficiency of symbolic regression of systems described by equations of similar complexity. Within the paper, the source of this behaviour is identified in presence of struc- tures which are hard to be discovered during evolutionary process due to low probability of their occurrence in initial population and by low chance to produce them by standard evolutionary operators given by low probability to form them in single step and low fitness function magnitudes of inter-steps when GPA tries to form them in more steps. This low magnitude of fitness function for particular so- lutions tends to elimination of them thus increases number of needed evolutionary steps. The results are significant for prediction of unknown system states on the base of its model symbolic regression on the base of its previous states. (en)
Title
  • Specific Behaviour of GPA-ES Evolutionary System Observed in Deterministic Chaos Regression
  • Specific Behaviour of GPA-ES Evolutionary System Observed in Deterministic Chaos Regression (en)
skos:prefLabel
  • Specific Behaviour of GPA-ES Evolutionary System Observed in Deterministic Chaos Regression
  • Specific Behaviour of GPA-ES Evolutionary System Observed in Deterministic Chaos Regression (en)
skos:notation
  • RIV/61989100:27240/13:86083873!RIV14-MSM-27240___
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  • P(EE.2.3.20.0072), S
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  • 106882
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  • RIV/61989100:27240/13:86083873
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  • Regression; Chaos; Deterministic; Observed; System; Evolutionary; GPA-ES; Behaviour; Specific (en)
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  • [5E8BCBAB9E35]
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  • Ostrava
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  • Berlin
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  • Advances in Intelligent Systems and Computing
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  • Zelinka, Ivan
  • Brandejský, Tomáš
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issn
  • 2194-5357
number of pages
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  • Springer-Verlag
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  • 978-3-642-33226-5
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  • 27240
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