Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Pattern Graphs: Combining Multivariate Time Series and Labelled Interval Sequences for Classification Read chapter Read first chapter

2013 | OriginalPaper | Chapter

The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks

Authors : Andrew James Turner, Julian Francis Miller

Published in: Research and Development in Intelligent Systems XXX

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

NeuroEvolution (NE) is the application of evolutionary algorithms to Artificial Neural Networks (ANN). This paper reports on an investigation into the relative importance of weight evolution and topology evolution when training ANN using NE. This investigation used the NE technique Cartesian Genetic Programming of Artificial Neural Networks (CGPANN). The results presented show that the choice of topology has a dramatic impact on the effectiveness of NE when only evolving weights; an issue not faced when manipulating both weights and topology. This paper also presents the surprising result that topology evolution alone is far more effective when training ANN than weight evolution alone. This is a significant result as many methods which train ANN manipulate only weights.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Formulating the Temporal Causal Relationships Between Events and Their Results
next chapter Inferring Context from Users’ Reviews for Context Aware Recommendation
Footnotes
1
Both CGP and ANNs can also be structured in a recurrent form.
 
2
Fully connected between layers i.e. a node in hidden layer two has an input from every node in hidden layer one.
 
3
If the arity is set high enough however all topologies are possible as each node can lower its own arity by only utilizing the first of multiple connections between two nodes.
 
Literature
1.
P. Angeline, G. Saunders, and J. Pollack. An Evolutionary Algorithm that Constructs Recurrent Neural Networks. IEEE Transactions on Neural Networks, 5(1):54–65, 1994.
2.
D. Floreano, P. Dürr, and C. Mattiussi. Neuroevolution: from Architectures to Learning. Evolutionary Intelligence, 1(1):47–62, 2008.
3.
X. Glorot and Y. Bengio. Understanding the Difficulty of Training Deep Feedforward Neural Networks. In Proceedings of the International Conference on Artificial Intelligence and Statistics (AISTATS10). Society for Artificial Intelligence and, Statistics, 2010.
4.
C. Igel. Neuroevolution for Reinforcement Learning using Evolution Strategies. In Evolutionary Computation, volume 4, pages 2588–2595. IEEE, 2003.
5.
M. M. Khan, G. M. Khan, and J. F. Miller. Evolution of Neural Networks using Cartesian Genetic Programming. In Proceedings of IEEE World Congress on Computational Intelligence, 2010.
6.
H. Larochelle, Y. Bengio, J. Louradour, and P. Lamblin. Exploring Strategies for Training Deep Neural Networks. The Journal of Machine Learning Research, 10:1–40, 2009.
7.
J. Miller and S. Smith. Redundancy and Computational Efficiency in Cartesian Genetic Programming. IEEE Transactions on Evolutionary Computation, 10(2):167–174, 2006.
8.
J. Miller and P. Thomson. Cartesian Genetic Programming. In Proceedings of the Third European Conference on Genetic Programming (EuroGP2000), volume 1802, pages 121–132. Springer-Verlag, 2000.
9.
J. F. Miller. What bloat? Cartesian Genetic Programming on Boolean Problems. In 2001 Genetic and Evolutionary Computation Conference Late Breaking Papers, pages 295–302, 2001.
10.
J. F. Miller, editor. Cartesian Genetic Programming. Springer, 2011.
11.
D. Moriarty and R. Mikkulainen. Efficient Reinforcement Learning through Symbiotic Evolution. Machine learning, 22(1):11–32, 1996.
12.
R. Poli. Some Steps Towards a Form of Parallel Distributed Genetic Programming. In Proceedings of the First On-line Workshop on, Soft Computing, pages 290–295, 1996.
13.
K. Stanley and R. Miikkulainen. Evolving Neural Networks through Augmenting Topologies. Evolutionary computation, 10(2):99–127, 2002.
14.
S. Thrun, J. Bala, E. Bloedorn, I. Bratko, B. Cestnik, J. Cheng, K. De Jong, S. Dzeroski, S. Fahlman, D. Fisher, et al. The Monk’s Problems a Performance Comparison of Different Learning Algorithms. Technical report, Carnegie Mellon University, 1991.
15.
A. J. Turner and J. F. Miller. Cartesian Genetic Programming encoded Artificial Neural Networks: A Comparison using Three Benchmarks. In Proceedings of the Conference on Genetic and Evolutionary Computation (GECCO-13), pages 1005–1012. ACM, 2013.
16.
A. Vargha and H. D. Delaney. A Critique and Improvement of the CL Common Language Effect Size Statistics of McGraw and Wong. Journal of Educational and Behavioral Statistics, 25(2):101–132, 2000.
17.
V. K. Vassilev and J. F. Miller. The Advantages of Landscape Neutrality in Digital Circuit Evolution. In Proc. International Conference on Evolvable Systems, volume 1801 of LNCS, pages 252–263. Springer Verlag, 2000.
18.
A. Wieland. Evolving Neural Network Controllers for Unstable Systems. In Neural Networks, 1991, IJCNN-91-Seattle International Joint Conference on, volume 2, pages 667–673. IEEE, 1991.
19.
X. Yao. Evolving Artificial Neural Networks. Proceedings of the IEEE, 87(9):1423–1447, 1999.
20.
T. Yu and J. F. Miller. Neutrality and the Evolvability of a Boolean Function Landscape. Genetic programming, pages 204–217, 2001.
Metadata
Title
The Importance of Topology Evolution in NeuroEvolution: A Case Study Using Cartesian Genetic Programming of Artificial Neural Networks
Authors
Andrew James Turner
Julian Francis Miller
Copyright Year
2013
Book
Research and Development in Intelligent Systems XXX

Print ISBN: 978-3-319-02620-6

Electronic ISBN: 978-3-319-02621-3

Copyright Year: 2013

DOI
https://doi.org/10.1007/978-3-319-02621-3_15

Premium Partner

    Image Credits