Generation of neural networks using a genetic algorithm approach
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- @Article{Trujillo-Romero:2013:IJBIC,
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author = "Felipe Trujillo-Romero",
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title = "Generation of neural networks using a genetic
algorithm approach",
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journal = "International Journal of Bio-Inspired Computation",
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year = "2013",
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month = oct # "~17",
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volume = "5",
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number = "5",
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pages = "289--302",
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keywords = "genetic algorithms, genetic programming, GP, neural
networks, evolutionary algorithms, evolutionary entity,
alpha-numeric character recognition, classification.",
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ISSN = "1758-0374",
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bibsource = "OAI-PMH server at www.inderscience.com",
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language = "eng",
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publisher = "Inderscience Publishers",
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URL = "http://www.inderscience.com/link.php?id=57183",
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DOI = "DOI:10.1504/IJBIC.2013.057183",
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abstract = "This paper discusses the generation of neural networks
that are obtained from the evolution of individual's
population in a genetic algorithm. For achieving this,
the population of individuals for the genetic algorithm
is formed of structural elements which constitute the
neural networks. These elements include the number of
layers, neurons per layer, transfer functions and the
connections between neurons in the network, among
others. These individuals as can be seen a structure
which has the ability to evolve rather than a standard
genotype. Furthermore, the size of the individuals is
not defined and depends mainly on the neural network
which in turn depends on the problem to be solved. This
structure considered as an evolutionary entity, is able
to evolve until convergence towards a suitable
structure is achieved. The fitness function is
specified with the features of the problem to be solved
by the neural network. This algorithm has been tested
successfully in solving classification problems, as in
the case of alpha-numerical character recognition, and
has been compared against a neural network obtained by
conventional means. Better results were obtained with
the neural network generated by using genetic
programming of this type of evolutionary entities.",
- }
Genetic Programming entries for
Felipe Trujillo-Romero
Citations