A general framework for encoding and evolving neural networks

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4667 LNAI, Issue , 2007, Pages 205-219

  Kassahun, Yohannes ^a   Metzen, Jan Hendrik ^a   De Gea, Jose ^a   Edgington, Mark ^a   Kirchner, Frank ^a,b  

^a UNIVERSITY OF BREMEN   (Germany)

^b GERMAN RESEARCH CENTER FOR ARTIFICIAL INTELLIGENCE DFKI   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ENCODING (SYMBOLS); FEATURE EXTRACTION; KNOWLEDGE REPRESENTATION; NETWORK ARCHITECTURE;

COMMON GENETIC ENCODING (CGE); GENETIC OPERATORS; INDIRECT ENCODING METHODS; MODULAR NETWORKS; PHENOTYPE NETWORKS;

NEURAL NETWORKS;

EID: 38149105156     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-74565-5_17     Document Type: Conference Paper

References (19)

1. Angeline, P.J., Saunders, G.M., Pollack, J.B.: An evolutionary algorithm that constructs recurrent neural networks. IEEE Transactions on Neural Networks 5, 54-65 (1994)
2. Bentley, P., Kumar, S.: Three ways to grow designs: A comparison of embryogenies for an evolutionary design problem. In: Banzhaf, W., Daida, J., Eiben, A.E., Garzon, M.H., Honavar, V., Jakiela, M., Smith, R.E. (eds.) Proceedings of the Genetic and Evolutionary Computation Conference, Orlando, Florida, USA, 13-17 July, 1999, vol. 1, pp. 35-43. Morgan Kaufmann, San Francisco (1999)
3. Bongard, J.C., Pfeifer, R.: Repeated structure and dissociation of genotypic and phenotypic complexity in artificial ontogeny. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2001, pp. 829-836 (2001)
4. Dellaert, F., Beer, R.D.: A developmental model for the evolution of complete autonomous agents. In: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pp. 393-401 (1996)
5. Gruau, F.: 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)
6. Hansen, N., Ostermeier, A.: Completely derandomized self-adaptation in evolution strategies. Evolutionary Computation 9(2), 159-195 (2001)
7. Jakobi, N.: Harnessing morphogenesis. In: Proceedings of Information Processing in Cells and Tissues, pp. 29-41 (1995)
8. Kassahun, Y.: 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)
9. Kassahun, Y., Edgington, M., Metzen, J.H., Sommer, G., Kirchner, F.: A common genetic encoding for both direct and indirect encodings of networks. In: Proceedings of the Genetic and Evolutionary Computation Conference, GECCO-2007 (accepted, July 2007)
10. Kitano, H.: Designing neural networks using genetic algoithms with graph generation system. Complex Systems 4, 461-476 (1990)
11. Lewis, F.L., Dawson, D.M., Abdallah, C.T.: Robot Manipulator Control: Theory and Practice. Marcel Dekker, Inc., New York, Basel (2004)
12. Lindenmayer, A.: Mathematical models for cellular interactions in development, parts I and II. Journal of Theoretical Biology 18, 280-315 (1968)
13. Luke, S., Spector, L.: Evolving graphs and networks with edge encoding: Preliminary report. In: Late-breaking papers of Genetic Programming 1996, Stanford, CA (1996)
14. Nolfi, S., Parisi, D.: Growing neural networks. Technical Report PCIA-91-15, Institute of Psychology, Rome (1991)
15. Schaffer, J., Whitley, L.D., Eshelmann, L.J.: Combination of genetic algorithms and neural networks: A survey of the state of the art. In: Proceedings of COGANN92 International Workshop on the Combination of Genetic Algorithm and Neural Networks, pp. 1-37. IEEE Computer Society Press, Los Alamitos (1992)
16. Sendhoff, B., Kreutz, M.: Variable encoding of modular neural networks for time series prediction. In: Congress on Evolutionary Computation (CEC'99), pp. 259-266 (1999)
17. Stanley, K.O.: Efficient Evolution of Neural Networks through Complexification. PhD thesis, Artificial Intelligence Laboratory. The University of Texas at Austin, Austin, USA (August 2004)
18. Vaario, J., Onitsuka, A., Shimohara, K.: Formation of neural structures. In: Proceedings of the Fourth European Conference on Articial Life, ECAL97, pp. 214-223 (1997)
19. Yao, X.: Evolving artificial neural networks. Proceedings of the IEEE 87(9), 1423-1447 (1999)