Searching for optimal size neural networks in Assembler Encoding

• Autorzy: Praczyk T.
• Języki publikacji: EN

Abstrakty

EN

Assembler Encoding represents a neural network in the form of a simple program called Assembler Encoding Program. The task of the program is to create the so-called Network Definition Matrix, which maintains all the information necessary to construct a network. To generate the programs and, in consequence, neural networks, evolutionary techniques are used. One of the problems in Assembler Encoding is to determine an optimal number of neurons in a neural network. To deal with this problem a current version of Assembler Encoding uses a solution that is time consuming and hence rather impractical. The paper proposes four other solutions to the problem mentioned. To test them, experiments in a predator-prey problem were carried out. The results of the experiments are included at the end of the paper.

Słowa kluczowe

EN

evolutionary neural networks

• Wydawca: Systems Research Institute, Polish Academy of Sciences
• Czasopismo: Control and Cybernetics
• Rocznik: 2010
• Tom: Vol. 39, no 4
• Strony: 1193--1215
• Opis fizyczny: Bibliogr. 17 poz., rys.

Twórcy

autor

Praczyk T.

• Polish Naval Academy, Institute of Naval Weapon, Gdynia, Poland,

Bibliografia

• CANGELOSI, A., PARISI, D. and NOLFI, S. (1994) Cell division and migration in a genotype for neural networks. Network: Computation in Neural Systems 5 (4), 497-515.
• GRUAU, F. (1994) Neural Network Synthesis Using Cellular Encoding And The Genetic Algorithm. PhD Thesis, Ecole Normale Superieure de Lyon.
• GRUAU, F. (1995) Automatic Definition of Modular Neural Networks. Adaptive Behavior 3(2), 151-183.
• GRUAU, F., WHITLEY, D. and PYEATT, L. (1996) A comparison between cellular encoding and direct encoding for genetic neural networks. In: J.R. Koza, D.E. Goldberg, D.B. Fogel and R.L. Riolo, eds., Genetic Programming: Proceedings of the First Annual Conference. MIT Press, 81-89.
• KITANO, H. (1990) Designing neural networks using genetic algorithms with graph generation system. Complex Systems 4, 461-476.
• KRAWIEC, K. and BHANU, B. (2005) Visual Learning by Coevolutionary Feature Synthesis. IEEE Trans, on Systems, Man, and Cybernetics, Part B: Cybernetics 35, 409-425.
• LUKE, S. and SPECTOR, L. (1996) Evolving Graphs and Networks with Edge Encoding: Preliminary Report. In: J.R. Koza, ed., Late Breaking Papers at the Genetic Programming 1996 Conference. Stanford University, CA, USA. Stanford Bookstore (1996), 117-124.
• MILLER, G.F., TODD, P.M., and HEGDE, S.U. (1989) Designing Neural Networks Using Genetic Algorithms. In: J.D. Schaffer, ed., Proceedings of the Third International Conference on Genetic Algorithms. Morgan Kaufmann, San Mateo, CA, 379-384.
• MORIARTY, D.E. and MIIKKULAINEN, R. (1998) Forming Neural Networks Through Efficient and Adaptive Coevolution. Evolutionary Computation 5 (4), 373-399.
• MORIARTY, D.E. (1997) Symbiotic Evolution of Neural Networks in Sequential Decision Tasks. PhD thesis, The University of Texas at Austin, TR UT-AI97-257.
• NOLFI, S. and PARISI, D. (1992) Growing neural networks. In: C.G. Langton, ed., Artificial Life III. Addison-Wesley, Reading, MA.
• NORDIN, P., BANZHAF, W. and FRANCONE, F. (1999) Efficient Evolution of Machine Code for CISC Architectures using Blocks and Homologous Cross-over. In: L. Spector, W. Langdon, U. O’Reilly and P. Angeline, eds., Advances in Genetic Programming III, MIT Press, 275-299.
• POTTER, M. (1997) The Design and Analysis of a Computational Model of Cooperative Coevolution. PhD thesis, George Mason University, Fairfax, Virginia.
• POTTER, M.A. and DE JONG, K.A. (2000) Cooperative coevolution: An architecture for evolving coadapted subcomponents. Evolutionary Computation 8 (1), 1-29.
• PRACZYK, T. (2007a) Evolving co-adapted subcomponents in Assembler Encoding. International Journal of Applied Mathematics and Computer Science 17(4).
• PRACZYK, T. (2007b) Procedure application in Assembler Encoding. Archives of Control Science 17(LIII), 1, 71-91.
• PRACZYK, T. (2008) Modular networks in Assembler Encoding. Computational Methods in Science and Technology, CMST 14 (1), 27-38.