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Book Sections Year : 2020

Evolving Programs to Build Artificial Neural Networks

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Abstract

In general, the topology of Artificial Neural Networks (ANNs) is human-engineered and learning is merely the process of weight adjustment. However, it is well known that this can lead to sub-optimal solutions. Topology and Weight Evolving Artificial Neural Networks (TWEANNs) can lead to better topologies however, once obtained they remain fixed and cannot adapt to new problems. In this chapter, rather than evolving a fixed structure artificial neural network as in neuroevolution, we evolve a pair of programs that build the network. One program runs inside neurons and allows them to move, change, die or replicate. The other is executed inside dendrites and allows them to change length and weight, be removed, or replicate. The programs are represented and evolved using Cartesian Genetic Programming. From the developed networks multiple traditional ANNs can be extracted, each of which solves a different problem. The proposed approach has been evaluated on multiple classification problems.
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Dates and versions

hal-02960439 , version 1 (07-10-2020)

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Julian Miller, Dennis G. Wilson, Sylvain Cussat-Blanc. Evolving Programs to Build Artificial Neural Networks. Adamatzky; Andrew and Kendon; Viv. ECC : Emergence, Complexity and Computation book series, 35, Springer, pp.23--71, 2020, From Astrophysics to Unconventional Computation, 978-3-030-15791-3. ⟨10.1007/978-3-030-15792-0_2⟩. ⟨hal-02960439⟩
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