Evolution of Cartesian Genetic Programs for Development of Learning Neural Architecture
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- @Article{Khan:2011:EC,
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author = "Gul Muhammad Khan and Julian F. Miller and
David M. Halliday",
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title = "Evolution of Cartesian Genetic Programs for
Development of Learning Neural Architecture",
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journal = "Evolutionary Computation",
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year = "2011",
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volume = "19",
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number = "3",
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pages = "469--523",
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keywords = "genetic algorithms, genetic programming, cartesian
genetic programming, Artificial Neural Networks, ANN,
Co-evolution, Generative and developmental approaches,
Learning and memory",
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ISSN = "1063-6560",
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DOI = "doi:10.1162/EVCO_a_00043)",
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size = "55 pages",
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abstract = "Although artificial neural networks have taken their
inspiration from natural neurological systems they have
largely ignored the genetic basis of neural functions.
Indeed, evolutionary approaches have mainly assumed
that neural learning is associated with the adjustment
of synaptic weights. The goal of this paper is to use
evolutionary approaches to find suitable computational
functions that are analogous to natural subcomponents
of biological neurons and demonstrate that intelligent
behaviour can be produced as a result of this
additional biological plausibility. Our model allows
neurons, dendrites, and axon branches to grow or die so
that synaptic morphology can change and affect
information processing while solving a computational
problem. The compartmental model of neuron consists of
a collection of seven chromosomes encoding distinct
computational functions inside neuron. Since the
equivalent computational functions of neural components
are very complex and in some cases unknown, we have
used a form of genetic programming known as Cartesian
Genetic Programming (CGP) to obtain these functions. We
start with a small random network of soma, dendrites,
and neurites that develops during problem solving by
executing repeatedly the seven chromosomal programs
that have been found by evolution. We have evaluated
the learning potential of this system in the context of
a well known single agent learning problem, known as
Wumpus World. We also examined the harder problem of
learning in a competitive environment for two
antagonistic agents, in which both agents are
controlled by independent CGP Computational Networks
(CGPCN). Our results show that the agents exhibit
interesting learning capabilities.",
- }
Genetic Programming entries for
Gul Muhammad Khan
Julian F Miller
David M Halliday
Citations