Diverse Neural Architectures in Assembler Encoding

• Autorzy: Praczyk T.

Identyfikatory

DOI

10.12921/cmst.2014.20.01.21-34

• Języki publikacji: EN

Abstrakty

EN

The paper presents a neuro-evolutionary method called Assembler Encoding (AE) and proposes its several modifications. The main goal of the modifications is to ensure AE greater freedom in generating diverse neural architectures. To compare the modifications with each other and with the original method the particular case of the predator-prey problem has been discussed.

Słowa kluczowe

EN

evolutionary neural networks

• Wydawca: Institute of Bioorganic Chemistry Scientific Publishers OWN, Polish Academy of Sciences
• Czasopismo: Computational Methods in Science and Technology
• Rocznik: 2014
• Tom: Vol. 20, No. 1
• Strony: 21--34
• Opis fizyczny: Bibliogr. 29 poz., rys.

Twórcy

autor

Praczyk T.

• Institute of Naval Weapon, Polish Naval Academy 81-103 Gdynia, ul. Śmidowicza 69

Bibliografia

• [1] L. Baird III, Reinforcement Learning Through Gradient Descent, PhD thesis, Carnegie Mellon University, Pittsburgh (1999).
• [2] A. Cangelosi, D. Parisi, S. Nolfi, Cell division and migration in a genotype for neural networks, Network: computation in neural systems 5(4), 497-515 (1994).
• [3] T.J. Fossen, Guidance and Control of Ocean Vehicles, John Wiley and Sons Ltd. (1994).
• [4] D. E. Goldberg, Genetic algorithms in search, optimization and machine learning, Addison Wesley, Reading, Massachusetts (1989).
• [5] F. Gomez and R. Miikkulainen, Incremental evolution of complex general behavior, Adaptive Behavior 5, 317-342 (1997).
• [6] F. Gomez, J. Schmidhuber and R. Miikkulainen, Accelerated Neural Evolution through Cooperatively Coevolved Synapses, Journal of Machine Learning Research, 9, 937-965 (2008).
• [7] F. Gruau, Neural network Synthesis Using Cellular Encoding And The Genetic Algorithm, PhD Thesis, Ecole Normale Superieure de Lyon (1994).
• [8] H. Kitano, Designing neural networks using genetic algorithms with graph generation system, Complex Systems 4, 461-476 (1990).
• [9] K. Krawiec, B. Bhanu, Visual Learning by Coevolutionary Feature Synthesis, IEEE Trans. on Systems, Man, and Cybernetics, Part B: Cybernetics 35, 409-425 (2005).
• [10] T. Kubaty, L. Rowinski, Mine counter vehicles for Baltic Navy, Internet, http://www.underwater.pg.gda.pl
• [11] 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 1996 Conference, (Stanford University, CA, USA, Stanford Bookstore, 1996) 117-124.
• [12] G.F. Miller, P.M. Todd, S.U. Hegde, Designing Neural Networks Using Genetic Algorithms, Proceedings of the Third International Conference on Genetic Algorithms. 379-384. of Schaffer J.D. (1989)
• [13] D. E. Moriarty, Symbiotic Evolution of Neural Networks in Sequential Decision Tasks, PhD thesis, The University of Texas at Austin, TR UT-AI97-257 (1997)
• [14] S. Nolfi, D. Parisi, Growing neural networks, In C. G. Langton, ed., Artificial Life III, Addison-Wesley (1992)
• [15] P. Nordin, W. Banzhaf, F. Francone, Efficient Evolution of Machine Code for CISC Architectures using Blocks and Homologous Crossover, Advances in Genetic Programming III, MIT Press, L. Spector and W. Langdon and U. O’Reilly and P. Angeline, pages. 275-299 (1999)
• [16] M. Potter, The Design and Analysis of a Computational Model of Cooperative Coevolution, PhD thesis, George Mason University, Fairfax, Virginia (1997).
• [17] M. A. Potter, K. A. De Jong, Cooperative coevolution: An architecture for evolving coadapted subcomponents, Evolutionary Computation 8(1), 1-29 (2000).
• [18] T. Praczyk, Evolving co-adapted subcomponents in Assembler Encoding, International Journal of Applied Mathematics and Computer Science 17(4) (2007).
• [19] T. Praczyk, Modular networks in Assembler Encoding, CMST 14(1), 27-38 (2008).
• [20] T. Praczyk, Using assembler encoding to solve inverted pendulum problem, Computing and Informatics 28, 2009, pp. 895-912 (2009).
• [21] T. Praczyk, Forming Neural Networks by Means of Assembler Encoding, Intelligent Automation and Soft Computing, 17(3), 319-331 (2011).
• [22] T. Praczyk, P. Szymak, Decision System for a Team of Autonomous Underwater Vehicles – Preliminary Report, Neurocomputing, Neurocomputing 74(17): 3323-3334 (2011).
• [23] T. Praczyk Solving the pole balancing problem by means of assembler encoding, Journal of Intelligent and Fuzzy Systems, in press
• [24] W. Siler, J. Buckley, Fuzzy Expert Systems and Fuzzy Reasoning, John Wiley and Sons Ltd. (2005).
• [25] O. Stanley, Efficient Evolution of Neural Networks Through Complexification, PhD thesis, Department of Computer Science, The University of Texas at Austin, Technical Report AI-TR-04-314 (2004).
• [26] O. Stanley and R. Miikkulainen, Evolving neural networks through augmenting topologies, Evolutionary Computation 10, 99-127 (2002).
• [27] P. Szymak, Using of fuzzy logic method to control of underwater vehicle in inspection of oceanotechnical objects, Polish Neural Network Society, Artificial Intelligence and Soft Computing, 163-168 (2006).
• [28] P. Szymak, Simplified mathematical model of underwater vehicle and its control system, in Polish, Pomiary, Automatyka i Robotyka, No. 2 (2010).
• [29] D. Whitley, J. Kauth, GENITOR: A different genetic algorithm, In Proceedings of The Rocky Mountain Conference on Artificial Intelligence, 118-130 (1988).