[ACM Press Proceeding of the fifteenth annual conference - Amsterdam, The Netherlands (2013.07.06-2013.07.10)] Proceeding of the fifteenth annual conference on Genetic and evolutionary computation conference - GECCO '13 - Cartesian genetic programming encoded artificial neural networks

✍ Scribed by Turner, Andrew James; Miller, Julian Francis

• Book ID: 121421145
• Publisher: ACM Press
• Year: 2013
• Tongue: English
• Weight: 434 KB
• Category: Article
• ISBN: 1450319637
• DOI: 10.1145/2463372.2463484

✦ Synopsis

Neuroevolution, the application of evolutionary algorithms to artificial neural networks (ANNs), is well-established in machine learning. Cartesian Genetic Programming (CGP) is a graph-based form of Genetic Programming which can easily represent ANNs. Cartesian Genetic Programming encoded ANNs (CGPANNs) can evolve every aspect of an ANN: weights, topology, arity and node transfer functions. This makes CGPANNs very suited to situations where appropriate configurations are not known in advance. The effectiveness of CGPANNs is compared with a large number of previous methods on three benchmark problems. The results show that CGPANNs perform as well as or better than many other approaches. We also discuss the strength and weaknesses of each of the three benchmarks.