Digital Library of the European Council for Modelling and Simulation

Title:	Pseudo Neural Networks Via Analytic Programming With Direct Coding Of Constant Estimation
Authors:	Zuzana Kominkova Oplatkova, Adam Viktorin, Roman Senkerik
Published in:	(2018). ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0005 ISSN: 2522-2422 (ONLINE) ISSN: 2522-2414 (PRINT) ISSN: 2522-2430 (CD-ROM) 32nd European Conference on Modelling and Simulation, Wilhelmshaven, Germany, May 22^nd – May 26^5h, 2018
Citation format:	Zuzana Kominkova Oplatkova, Adam Viktorin, Roman Senkerik (2018). Pseudo Neural Networks Via Analytic Programming With Direct Coding Of Constant Estimation, ECMS 2018 Proceedings Edited by: Lars Nolle, Alexandra Burger, Christoph Tholen, Jens Werner, Jens Wellhausen European Council for Modeling and Simulation. doi: 10.7148/2018-0143
DOI:	https://doi.org/10.7148/2018-0143
Abstract:	This research deals with a novel approach to classification – pseudo neural networks (PNN). This technique was inspired in classical artificial neural networks (ANN), where a relation between inputs and outputs is based on the mathematical transfer functions and optimised numerical weights. Compared to ANN, the whole structure in PNN, i.e. the relation between inputs and output(s), is fully synthesised by evolutionary symbolic regression tool – analytic programming. Compared to previous synthesised models, the PNN in this paper were synthesised via a new approach to constant estimation inside the analytic programming – direct coding. Iris data was used for the experiments and PNN were used for the synthesis of a complex classifier for more classes. For experimentation, Differential Evolution (de/rand/1/bin) for optimisation in analytic programming (AP) was used.
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