A Neuro-genetic approach using simple gene regulatory network to construct a distributed and nested adaptive neural network

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Date
2008-06
Authors
Rahmat, Romi Fadillah
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Abstract
For a long time neural networks have been a popular approach for intelligent machines development and knowledge discovery. However, problems still exists in neural networks, such as fixed architecture and excessive training time. One of the solutions to unravel this problem is by using neuro-genetic approach. A neurogenetic approach is inspired by a theory in neuroscience which state that the evolution of human brain structure is significantly affected by its genome structure. Hence, the structure and performance of a neural network should be based on a gene created for it. Therefore, to overcome these existing neural network problems and with the new theory of neuroscience in mind, in this thesis we will attempt to propose a neuro-genetic approach by using simple Gene Regulatory Network (GRN) as a more biologically plausible model of neural network. In realizing our proposed neuro-genetic approach, we ended up with two methods. Firstly, we proposed GRTE, a gene regulatory training engine to control, evaluate, mutate and train genes. Secondly, ANNN, a distributed and nested adaptive neural network that will be constructed based on the genes from GRTE. ANNN can be applied in two modes, ANNN Correlated Data and ANNN Uncorrelated Data. We conducted two experiments to evaluate and validate our neuro-genetic approach. In the first experiment, 8-bits XOR parity problems were used as a dataset, while in the second experiment, the Proben I 's Gene Benchmark Datasets were used. The results of the experiments validate the objective of this work.
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Keywords
Genetic approach using simple gene regulatory , Nested adaptive neural network
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