Breaking the Stereotypical Dogma of Artificial Neural Networks with Cartesian Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8129
- @InCollection{Khan:2017:miller,
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author = "Gul Muhammad Khan and Arbab Masood Ahmad",
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title = "Breaking the Stereotypical Dogma of Artificial Neural
Networks with Cartesian Genetic Programming",
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booktitle = "Inspired by Nature: Essays Presented to Julian F.
Miller on the Occasion of his 60th Birthday",
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publisher = "Springer",
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year = "2017",
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editor = "Susan Stepney and Andrew Adamatzky",
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volume = "28",
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series = "Emergence, Complexity and Computation",
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chapter = "10",
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pages = "213--233",
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keywords = "genetic algorithms, genetic programming, Cartesian
Genetic Programming, ANN",
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isbn13 = "978-3-319-67996-9",
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DOI = "doi:10.1007/978-3-319-67997-6_10",
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abstract = "This chapter presents the work done in the field of
Cartesian Genetic Programming evolved Artificial Neural
Networks (CGPANN). Three types of CGPANN are presented,
the Feed-forward CGPANN (FFCGPAN), Recurrent CGPANN and
the CGPANN that has developmental plasticity, also
called Plastic CGPANN or PCGPANN. Each of these
networks is explained with the help of diagrams.
Performance results obtained for a number of benchmark
problems using these networks are illustrated with the
help of tables. Artificial Neural Networks (ANNs)
suffer from the dilemma of how to select complexity of
the network for a specific task, what should be the
pattern of inter-connectivity, and in case of feedback,
what topology will produce the best possible results.
Cartesian Genetic Programming (CGP) offers the ability
to select not only the desired network complexity but
also the inter-connectivity patterns, topology of
feedback systems, and above all, decides which input
parameters should be weighted more or less and which
one to be neglected. In this chapter we discuss how CGP
is used to evolve the architecture of Neural Networks
for optimum network and characteristics. Don't you want
a system that designs everything for you? That helps
you select the optimal network, the inter-connectivity,
the topology, the complexity, input parameters
selection and input sensitivity? If yes, then CGP
evolved Artificial Neural Network (CGPANN) and CGP
evolved Recurrent Neural Network (CGPRNN) is the answer
to your questions.",
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notes = "part of \cite{miller60book}
https://link.springer.com/bookseries/10624",
- }
Genetic Programming entries for
Gul Muhammad Khan
Arbab Masood Ahmad
Citations