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Digital Library of the
European Council for Modelling and Simulation |
Title: |
Iris Data Classification By
Means Of Pseudo Neural Networks Based On Evolutionary Symbolic Regression |
Authors: |
Zuzana Kominkova Oplatkova,
Roman Senkerik |
Published in: |
(2013).ECMS 2013 Proceedings edited
by: W. Rekdalsbakken, R. T. Bye, H. Zhang European Council for Modeling
and Simulation. doi:10.7148/2013 ISBN:
978-0-9564944-6-7 27th
European Conference on Modelling and Simulation, Aalesund, Norway, May 27th –
30th, 2013 |
Citation
format: |
Zuzana Kominkova
Oplatkova, Roman Senkerik
(2013). Iris Data Classification By Means Of Pseudo Neural Networks Based On
Evolutionary Symbolic Regression, ECMS 2013 Proceedings
edited by: W. Rekdalsbakken, R. T. Bye, H. Zhang, European Council for Modeling
and Simulation. doi:10.7148/2013-0355 |
DOI: |
http://dx.doi.org/10.7148/2013-0355 |
Abstract: |
This research deals with a
novel approach to classification. Iris data was used for the experiments. Classical
artificial neural networks, where a relation between inputs and outputs is
based on the mathematical transfer functions and optimized numerical weights,
was an inspiration for this work. Artificial neural networks need to optimize
weights, but the structure and transfer functions are usually set up before
the training. The proposed method utilizes the symbolic regression for
synthesis of a whole structure, i.e. the relation between inputs and
output(s) and tested on iris data in this case. For experimentation, Differential
Evolution (DE) for the main procedure and also for meta-evolution version of
analytic programming (AP) was used. |
Full
text: |