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Procedure application in assembler encoding

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Języki publikacji
EN
Abstrakty
EN
In order to use evolutionary techniques to search for optimal neural networks it is necessary to encode the latter in the form of chromosome or a set of chromosomes. In the paper a new neural network encoding method is presented - assembler encoding (AE). It assumes neural network encoded in the form of linearly organized structure similar to assembler program with code part and with data part. The task of assembler code is to create connectivity matrix which in turn can be transferred into neural network with any architecture. In the article the variant of AE in which we deal with application of procedures is discussed. Assembler encoding programs consisting of many procedures are used to solve optimization problem. Results of tests conducted are included in the paper.
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71--91
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Bibliogr. 14 poz., rys., tab.
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Bibliografia
  • [1] A. CANGELOSI, D. PARISI and S. NOLFI: Cell division and migration in a ‘genotype’ for neural networks. Network: computation in neural systems, 5(4), (1994).
  • [2] D. FLOREANO and J. URZELAI: Evolutionary robots with online self-organization and behavioral fitness. Neural Networks, 13 (2000), 431-443.
  • [3] D. FLOREANO and J. URZELAI: Neural morphogenesis, synaptic plasticity and evolution. Scientific Literature Digital Library - http://citeseer.ist.psu.edu
  • [4] F. GRUAU: Neural network synthesis using cellular encoding and the genetic algorithm. PhD Thesis, Ecole Normale Superieure de Lyon, 1994.
  • [5] H. KITANO: Designing neural networks usin g genetic algorithms with graph generation system. Complex Systems, 4 ( 1990), 461-476.
  • [6] S. LUKE and L. SPECTOR: Evolving graphs and networks with edge encoding: Preliminary report. In John R. Koza, (Ed.) Late Breaking Papers at the Genetic Programming. Conf. Stanford University, Stanford University, CA, USA, (1996), 117-124.
  • [7] G. F. MILLER, P. M. TODD and S. U. HEGDE: Designing neural networks using genetic algorithms. Proc. of the Third Mt. Conf. Genetic Algorithms, (1989), 379-384.
  • [8] D. E. MORIARTY and R. MIIKKULAINEN: Forming neural networks through efficient and adaptive coevolution. Evolutionary Computation, 5(4), (1998), 373-399.
  • [9] D. E. MORIARTY: Symbiotic evolution of neural networks in sequential decision tasks. PhD thesis, The University of Texas at Austin, TR UT-A197-257, 1997.
  • [10] S. NOLFI and D. PARISI: Growing neural networks. Technical Report, Institute of Psychology, CNR Rome, 1992.
  • [11] T. PRACZYK: An application of assembler encoding to optimization problem. (to appear).
  • [12] T. PRACZYK: Evolving co-adapted subcomponents in assembler encoding. Int. of Applied Mathematics and Computer Science, (in printing).
  • [13] J. W. PRIOR: Eugenic evolution for combinatorial optimization. Master's thesis, The University of Texas at Austin, TR A198-268, 1998.
  • [14] J. URZELAI and D. FLOREANO: Evolution of adaptive synapses: Robots with fast adaptive behavior in new environments. Evolutional .), Computation, 9(4), (2001), 495-524.
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Bibliografia
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bwmeta1.element.baztech-article-BSW3-0037-0005
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