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  • This paper focuses on so-called TWEANNs (Toppology and Weight Evolving Artificial Neural Networks). Here, we concentrate on a use of an indirect developmental encoding which is an approach inspired by multi-cellular organisms' development from a single cell. We examine multiple modifications of a known tree-based indirect developmental encoding: the Cellular Encoding. Grammatical Evolution (GE) is employed instead of Genetic Programming (GP) to optimize program trees. GE is advantageous mainly in the way it can handle constraints (as it evolves program trees which conform to a grammar prespecified using a BNF notation). Moreover, we employ GE's inner mechanisms to efficiently encode Neural Network parameters (weights and biases). In this work, we compare three different link selection schemes. The results of our investigations show that our modifications of Cellular Encoding improve the ability to evolve real-valued Artificial Neural Networks.
  • This paper focuses on so-called TWEANNs (Toppology and Weight Evolving Artificial Neural Networks). Here, we concentrate on a use of an indirect developmental encoding which is an approach inspired by multi-cellular organisms' development from a single cell. We examine multiple modifications of a known tree-based indirect developmental encoding: the Cellular Encoding. Grammatical Evolution (GE) is employed instead of Genetic Programming (GP) to optimize program trees. GE is advantageous mainly in the way it can handle constraints (as it evolves program trees which conform to a grammar prespecified using a BNF notation). Moreover, we employ GE's inner mechanisms to efficiently encode Neural Network parameters (weights and biases). In this work, we compare three different link selection schemes. The results of our investigations show that our modifications of Cellular Encoding improve the ability to evolve real-valued Artificial Neural Networks. (en)
Title
  • Grammatical Evolution for Development of Neural Networks with Real-valued Weights Using Cellular Encoding
  • Grammatical Evolution for Development of Neural Networks with Real-valued Weights Using Cellular Encoding (en)
skos:prefLabel
  • Grammatical Evolution for Development of Neural Networks with Real-valued Weights Using Cellular Encoding
  • Grammatical Evolution for Development of Neural Networks with Real-valued Weights Using Cellular Encoding (en)
skos:notation
  • RIV/68407700:21230/08:00146975!RIV10-MSM-21230___
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(KJB201210701), Z(MSM6840770012)
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 369591
http://linked.open...ai/riv/idVysledku
  • RIV/68407700:21230/08:00146975
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • Cellular Encoding; Grammatical Evolution; Neural networks (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...ontrolniKodProRIV
  • [00C06AE7963E]
http://linked.open...v/mistoKonaniAkce
  • Le Havre
http://linked.open...i/riv/mistoVydani
  • Ghent
http://linked.open...i/riv/nazevZdroje
  • European Simulation and Modelling Conference 2008
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...iv/tvurceVysledku
  • Drchal, Jan
  • Šnorek, Miroslav
http://linked.open...vavai/riv/typAkce
http://linked.open...ain/vavai/riv/wos
  • 000264749400028
http://linked.open.../riv/zahajeniAkce
http://linked.open...n/vavai/riv/zamer
number of pages
http://purl.org/ne...btex#hasPublisher
  • EUROSIS - ETI
https://schema.org/isbn
  • 978-90-77381-44-1
http://localhost/t...ganizacniJednotka
  • 21230
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