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2013 | OriginalPaper | Buchkapitel

Evolutionary Synthesis of Complex Structures – Pseudo Neural Networks for the Task of Iris Dataset Classification

verfasst von: Zuzana Kominkova Oplatkova, Roman Senkerik

Erschienen in: Nostradamus 2013: Prediction, Modeling and Analysis of Complex Systems

Verlag: Springer International Publishing

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This research deals with a novel approach to classification. This paper deals with a synthesis of a complex structure which serves as a classifier. Classical artificial neural networks, where a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights, were an inspiration for this work. The proposed method utilizes Analytic Programming (AP) as the tool of the evolutionary symbolic regression. AP synthesizes a whole structure of the relation between inputs and output. Iris data (a known benchmark for classifiers) was used for testing of the proposed method. For experimentation, Differential Evolution for the main procedure and also for meta-evolution version of analytic programming was used.

Metadaten
Titel
Evolutionary Synthesis of Complex Structures – Pseudo Neural Networks for the Task of Iris Dataset Classification
verfasst von
Zuzana Kominkova Oplatkova
Roman Senkerik
Copyright-Jahr
2013
Verlag
Springer International Publishing
DOI
https://doi.org/10.1007/978-3-319-00542-3_22

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