Artificial neural network development by means of a novel combination of grammatical evolution and genetic algorithm

Created by W.Langdon from gp-bibliography.bib Revision:1.4549

  author =       "Fardin Ahmadizar and Khabat Soltanian and 
                 Fardin AkhlaghianTab and Ioannis Tsoulos",
  title =        "Artificial neural network development by means of a
                 novel combination of grammatical evolution and genetic
  journal =      "Engineering Applications of Artificial Intelligence",
  year =         "2015",
  volume =       "39",
  bibdate =      "2015-02-23",
  bibsource =    "DBLP,
  pages =        "1--13",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution, Neural networks, ANN, Adaptive penalty
                 approach, Classification problems",
  ISSN =         "0952-1976",
  URL =          "",
  URL =          "",
  abstract =     "The most important problems with exploiting artificial
                 neural networks (ANNs) are to design the network
                 topology, which usually requires an excessive amount of
                 expert's effort, and to train it. In this paper, a new
                 evolutionary-based algorithm is developed to
                 simultaneously evolve the topology and the connection
                 weights of ANNs by means of a new combination of
                 grammatical evolution (GE) and genetic algorithm (GA).
                 GE is adopted to design the network topology while GA
                 is incorporated for better weight adaptation. The
                 proposed algorithm needs to invest a minimal expert's
                 effort for customisation and is capable of generating
                 any feedforward ANN with one hidden layer. Moreover,
                 due to the fact that the generalisation ability of an
                 ANN may decrease because of over fitting problems, the
                 algorithm uses a novel adaptive penalty approach to
                 simplify ANNs generated through the evolution process.
                 As a result, it produces much simpler ANNs that have
                 better generalization ability and are easy to
                 implement. The proposed method is tested on some real
                 world classification datasets, and the results are
                 statistically compared against existing methods in the
                 literature. The results indicate that our algorithm
                 outperforms the other methods and provides the best
                 overall performance in terms of the classification
                 accuracy and the number of hidden neurons. The results
                 also present the contribution of the proposed penalty
                 approach in the simplicity and generalisation ability
                 of the generated networks.",
  notes =        "also known as \cite{AHMADIZAR20151}",

Genetic Programming entries for Fardin Ahmadizar Khabat Soltanian Fardin Akhlaghian Tab Ioannis G Tsoulos