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EANT2 ("Evolutionary Acquisition of Neural Topologies, Version 2") is a system for evolutionary reinforcement learning of artificial neural networks. It develops both the structure (topology) and the parameters (e.g. synaptic weights) of neural networks to create solutions for a given task. It has the following features:
- The structural and parametrical development of the neural networks is clearly separated so as to enable the system to quickly develop useful network structures.
- Neural Networks are encoded in a Linear Genome. Genes can represent neurons (with an arbitrary activation function), connections, biases etc. This encoding is compact and versatile; it facilitates manipulation and evaluation without decoding.
- Parameter optimisation is done using CMA-ES, a derandomised variant of evolution strategies.
- EANT2 requires no training data pairs for network training (as supervised/unsupervised/semi-supervised methods do) but instead uses Reinforcement Learning to adapt to the structure of the problem by means of a scalar fitness function.
- There is no parameter tuning to a given problem; the method is designed to be as general as possible.
EANT2 is the name of the newer version of the algorithm which is based on the original version "EANT" by Yohannes Kassahun. More information on EANT and EANT2 as well as comparisons with different methods can be found on this page on evolutionary reinforcement learning and in the relevant references (see below). mehr... |
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| Publications: | | 2008 | Evolutionary Learning of Neural Structures for Visuo-Motor Control
Siebel, N., Sommer, G., Kassahun, Y.
In Arpad Kelemen, Ajith Abraham and Yulan Liang (eds.), Computational Intelligence in Medical Informatics, pp. 93-115, ISBN 3-540-75766-5, Springer-Verlag Berlin, 2008 | PDF, PS , BibTeX, Abstract |
| 2007 | Efficient learning of neural networks with evolutionary algorithms
Siebel, N., Krause, J., Sommer, G.
In Pattern Recognition, F. Hamprecht, B. Jähne, C. Schnörr (Eds.), Heidelberg, LNCS, Vol. 4713, pp. 466-475, Springer-Verlag, Berlin. DOI: 10.1007/978-3-540-74936-3_47 | PDF , BibTeX |
| 2007 | Self-organisation of neural topologies by evolutionary reinforcement learning
Siebel, N., Krause, J., Sommer, G.
In Proceedings of the 6th International Workshop on Self-Organising Maps (WSOM 2007), Bielefeld, Germany, 7 pages (no page numbers), September 2007. DOI: 10.2390/biecoll-wsom2007-118. | PDF , BibTeX, Abstract |
| 2007 | Evolutionary reinforcement learning of artificial neural networks
Siebel, N., Sommer, G.
In International Journal of Hybrid Intelligent Systems (IJHIS), IOS Press, 4(3), pp. 171-183, October 2007. | PDF, PS , BibTeX, Abstract |
| 2006 | Learning neural networks for visual servoing using evolutionary methods
Siebel, N., Kassahun, Y.
In Proceedings of the 6th International Conference on Hybrid Intelligent Systems (HIS'06), Auckland, New Zealand, p. 6 (4 pages), December 2006, ISBN 0-7695-2662-4, DOI: 10.1109/HIS.2006.41. | PDF , BibTeX, Abstract |
| 2006 | Towards a Unified Approach to Learning and Adaptation
Kassahun, Y.
Dissertation, Institut für Informatik und Praktische Mathematik, Christian-Albrechts-Universität zu Kiel, 2006. | PDF , BibTeX |
| 2006 | Towards a Unified Approach to Learning and Adaptation
Kassahun, Y.
Technical Report 0602, Institut für Informatik und Praktische Mathematik, Christian-Albrechts-Universität zu Kiel, 2006. | PDF , BibTeX |
| 2006 | Evolutionary reinforcement learning for simulated locomotion of a robot with a two-link arm
Kassahun, Y., Sommer, G.
In Proc. of the 9th Conference on Intelligent Autonomous Systems IAS-9, March 7-9, T. Arai, R. Pfeifer, T. Balch and H. Yokoi (Eds.), pp.263-271, IOS Press, 2006. | PDF , BibTeX |
| 2005 | Efficient reinforcement learning through evolutionary acquisition of neural topologies
Kassahun, Y., Sommer, G.
In Proc. 13th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 259-266, d-side publications, April 2005. | PDF , BibTeX, Abstract |
| 2005 | Efficient reinforcement learning through evolutionary acquisition of neural topologies
Kassahun, Y., Sommer, G.
In Proc. 13th European Symposium on Artificial Neural Networks, Bruges, Belgium, pp. 259-266, d-side publications, April 2005. | PDF , BibTeX, Abstract |
| 2005 | Evolution of Neural Networks Through Incremental Acquisition of Neural Structures
Kassahun, Y., Sommer, G.
Technical Report 0508, Christian-Albrechts-Universität zu Kiel, Institut für Informatik und Praktische Mathematik, Juni 2005. | PDF , BibTeX |
| 2005 | Automatic neural robot controller design using evolutionary acquisition of neural topologies
Kassahun, Y., Sommer, G.
In Proc. 19. Fachgespräch Autonome Mobile Systeme (AMS2005), Stuttgart,
pp. 315-321, Springer-Verlag, 2005 | PDF , BibTeX |
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