Pseudo Neural Networks Synthesized via Evolutionary Symbolic Regression for Pima Diabetes

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F70883521%3A28140%2F16%3A43875607" target="_blank" >RIV/70883521:28140/16:43875607 - isvavai.cz</a>
Result on the web
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DOI - Digital Object Identifier
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Result language
angličtina
Original language name
Pseudo Neural Networks Synthesized via Evolutionary Symbolic Regression for Pima Diabetes
Original language description
This research deals with pseudo neural networks which were applied for solving Pima diabetes set. Pseudo neural networks are complex expressions synthesized by means of an evolutionary symbolic regression technique - analytic programming (AP). It represents a novel approach to classification when a relation between inputs and outputs is created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. There is also an advantage of suitable feature set selection during the same step of pseudo neural net synthesis. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year
2016
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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