Incremental acquisition of neural structures through evolution
Yohannes Kassahun, Jan Hendrik Metzen, Mark Edgington, Frank Kirchner
Editors: Kay Chen Tan, Dikai Liu, Lingfeng Wang
In Design and Control of Intelligent Robotic Systems, Springer Verlag, 1, series Studies in Computational Intelligence, volume 177, chapter o.A., pages 187-208, Feb/2009. ISBN: 3540899324.
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.
neural networks, reinforcement learning, evolutionary methods