Skip to main content Skip to main navigation

Publikation

Incremental acquisition of neural structures through evolution

Yohannes Kassahun; Jan Hendrik Metzen; Mark Edgington; Frank Kirchner
In: Kay Chen Tan; Dikai Liu; Lingfeng Wang (Hrsg.). Design and Control of Intelligent Robotic Systems. Pages 187-208, Studies in Computational Intelligence, Vol. 177, ISBN 3540899324, Springer Verlag, 2/2009.

Zusammenfassung

In this contribution we present a novel method, called Evolutionary Acquisition of Neural Topologies (EANT), for evolving the structures and weights of neural networks. The method uses an efficient and compact genetic encoding of a neural network into a linear genome that enables a network’s outputs to be computed without the network being decoded. Furthermore, it uses a nature inspired metalevel evolutionary process where new structures are explored at a larger timescale, and existing structures are exploited at a smaller timescale. Because of this, the method is able to find minimal neural structures for solving a given learning task.