An examination of different fitness and novelty based selection methods for the evolution of neural networks-Reference-Cited by-同舟云学术

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An examination of different fitness and novelty based selection methods for the evolution of neural networks

Published:2012-11-21 Issue:5 Volume:17 Page:753-767
ISSN:1432-7643
Container-title:Soft Computing
language:en
Short-container-title:Soft Comput

Author:

Inden Benjamin,Jin Yaochu,Haschke Robert,Ritter Helge,Sendhoff Bernhard

Publisher

Springer Science and Business Media LLC

Subject

Geometry and Topology,Theoretical Computer Science,Software

Link

http://link.springer.com/content/pdf/10.1007/s00500-012-0960-z.pdf

Reference41 articles.

1. Alba E, Troya JM (2002) Improving flexibility and efficiency by adding parallelism to genetic algorithms. Stat Comput 12:91–114

2. Banzhaf W, Beslon G, Christensen S, Foster JA, Kps F, Lefort V, Miller JF, Radman M, Ramsden JJ (2006) From artificial evolution to computational evolution: a research agenda. Nat Rev Genet 7:729–735

3. Barton N, Partridge L (2000) Limits to natural selection. BioEssays 22:1075–1084

4. Buason G, Bergfeldt N, Ziemke T (2005) Brains, bodies and beyond: competitive co-evolution of robot controllers, morphologies and environments. Genet Program Evolvable Mach 6:25–51

5. Clune J, Beckmann BE, Ofria C, Pennock RT (2009) Evolving coordinated quadruped gaits with the hyperneat generative encoding. In: Proceedings of the IEEE congress on evolutionary computing

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