Incremental Evolution in ANNs: Neural Netswhich Grow

Authors:
Christopher Macleod;Grant M. Maxwell
Affiliations:
School of Electronic and Electrical Engineering, The Robert Gordon University, Aberdeen, UK (E-mail: c.macleod@eee.rgu.ac.uk);School of Electronic and Electrical Engineering, The Robert Gordon University, Aberdeen, UK
Venue:
Artificial Intelligence Review
Year:
2001

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Abstract

This paper explains the optimisation of neuralnetwork topology using Incremental Evolution;that is, by allowing the network to expand byadding to its structure. This method allows anetwork to grow from a simple to a complexstructure until it is capable of fulfilling itsintended function. The approach is somewhatanalogous to the growth of an embryo or theevolution of a fossil line through time, it istherefore sometimes referred to as anembryology or embryological algorithm. Thepaper begins with a general introduction,comparing this method to other competingtechniques such as The Genetic Algorithm, otherEvolutionary Algorithms and SimulatedAnnealing. A literature survey of previous workis included, followed by an extensive newframework for application of the technique.Finally, examples of applications and a generaldiscussion are presented.