A common genetic encoding for both direct and indirect encodings of networks

Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference

Volumn , Issue , 2007, Pages 1029-1036

  Kassahun, Yohannes ^a   Edgington, Mark ^a   Metzen, Jan Hendrik ^a   Sommer, Gerald ^b   Kirchner, Frank ^a  

^a UNIVERSITY OF BREMEN   (Germany)

^b UNIVERSITY OF KIEL   (Germany)

Author keywords

Genetic encoding; Genotype phenotype mapping

Indexed keywords

FORMAL METHODS; GENE ENCODING; PROBLEM SOLVING; TOPOLOGY;

GENETIC ENCODING; GENOTYPE; GENOTYPE PHENOTYPE MAPPING;

GENETIC ALGORITHMS;

EID: 34548077601     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1276958.1277162     Document Type: Conference Paper

References (10)

1. P. J. Angeline, G. M. Saunders, and J. B. Pollack. An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks, 5:54-65, 1994.
2. F. Gomez, J. Schmidhuber, and R. Miikkulainen. Efficient non-linear control through neuroevolution. In Proceedings of the European Conference on Machine Learning (ECML 2006), 2006.
3. F. Gruau. Neural Network Synthesis Using Cellular Encoding and the Genetic Algorithm. PhD thesis, Ecole Normale Superieure de Lyon, Laboratoire de l'Informatique du Parallelisme, France, January 1994.
4. Y. Kassahun. Towards a Unified Approach to Learning and Adaptation. PhD thesis, Technical Report 0602, Institute of Computer Science and Applied Mathematics, Christian-Albrechts University, Kiel, Germany, February 2006.
5. S. Luke and L. Spector. Evolving graphs and networks with edge encoding: Preliminary report. In Late-breaking papers of Genetic Programming 1996. Stanford, CA, 1996.
6. S. Nolfi and D. Floreano. Evolutionary Robotics. The Biology, Intelligence, and Technology of Self-Organizing Machines. MIT Press, Massachusetts, London, 2000.
7. J. Reisinger, K. O. Stanley, and R. Miikkulainen. Evolving reusable neural modules. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO-2004), pages 69-81, 2004.
8. K. O. Stanley. Efficient Evolution of Neural Networks through Complexification. PhD thesis, Artificial Intelligence Laboratory. The University of Texas at Austin., Austin, USA, August 2004.
9. M. E. Taylor, S. Whiteson, and P. Stone. Comparing evolutionary and temporal difference methods in a reinforcement learning domain. In GECCO '06: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pages 1321-1328, New York, NY, USA, 2006. ACM Press.
10. X. Yao. Evolving artificial neural networks. Proceedings of the IEEE, 87(9):1423-1447, 1999.