Incremental evolution in ANNs: Neural nets which grow

Christopher Macleod, Grant M. Maxwell

Research output: Contribution to journalArticlepeer-review

10 Citations (Scopus)


This paper explains the optimisation of neural network topology using Incremental Evolution; that is, by allowing the network to expand by adding to its structure. This method allows a network to grow from a simple to a complex structure until it is capable of fulfilling its intended function. The approach is somewhat analogous to the growth of an embryo or the evolution of a fossil line through time, it is therefore sometimes referred to as an embryology or embryological algorithm. The paper begins with a general introduction, comparing this method to other competing techniques such as The Genetic Algorithm, other Evolutionary Algorithms and Simulated Annealing. A literature survey of previous work is included, followed by an extensive new framework for application of the technique. Finally, examples of applications and a general discussion are presented.

Original languageEnglish
Pages (from-to)201-224
Number of pages24
JournalArtificial Intelligence Review
Issue number3
Publication statusPublished - Nov 2001


  • Artificial neural networks
  • Evolutionary programming
  • Evolutionary strategy
  • Genetic algorithms
  • Incremental evolution
  • Network growth


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