Artificial Neural Networks as Subsymbolic Process Descriptors

Created by W.Langdon from gp-bibliography.bib Revision:1.8098

@PhdThesis{Minns:thesis,

• author = "Anthony William Minns",
• title = "Artificial Neural Networks as Subsymbolic Process Descriptors",
• school = "UNESCO-IHE Institute for Water Education, Technische Universiteit Delft",
• year = "1998",
• address = "The Netherlands",
• keywords = "genetic algorithms, genetic programming, Rainfall-runoff Modelling, ANN, Cellular automata, Neural networks (Computer science), Logic circuits, Hydraulic engineering, artificial intelligence, artificial neural networks, EAs, LCS, lattice gas dynamics, Storm, Extreme Events, Cantley Estate in Doncaster, Silk Stream Hendon Lane, Dollis Brook Colindeep Lane, Catchment, Brent Reservoir, London Clay, UK, RORB, IMP",
• publisher = "Balkema, Rotterdam",
• isbn13 = "9054104090",
• URL = "https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=24a4bb0e44d938f847cdcdd8667c82820b342bb9",
• URL = "https://ihedelftrepository.contentdm.oclc.org/digital/collection/phd1/id/22089/",
• URL = "https://catalogue.nla.gov.au/catalog/1915787.dc_xml",
• size = "140 pages",
• notes = "Section 6.2 brief description of GP performance in comparison with ANN.

  p65 'extreme compactness of the GP expressions'

  UK Flood Studies Report (Natural Environment Research Council, 1975)",

}

Genetic Programming entries for Anthony W Minns

Citations