Diverse Neural Architectures in Assembler Encoding
Institute of Naval Weapon, Polish Naval Academy
81-103 Gdynia, ul. Smidowicza 69
E-mail: t.praczyk@amw.gdynia.pl
Received:
Received: 27 January 2014; revised: 22 March 2014; accepted: 22 March 2014; published online: 26 March 2014
DOI: 10.12921/cmst.2014.20.01.21-34
Abstract:
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.
Key words:
References:
[1] L. Baird III, Reinforcement Learning Through Gradient De-
scent, 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, optimiza-
tion and machine learning, Addison Wesley, Reading, Mas-
sachusetts (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, Accel-
erated Neural Evolution through Cooperatively Coevolved
Synapses, Journal of Machine Learning Research, 9, 937-965
(2008).
[7] F. Gruau, Neural network Synthesis Using Cellular Encod-
ing And The Genetic Algorithm, PhD Thesis, Ecole Normale
Superieure de Lyon (1994).
[8] H. Kitano, Designing neural networks using genetic algo-
rithms 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 Cy-
bernetics, 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 Con-
ference, (Stanford University, CA, USA, Stanford Bookstore,
1996) 117-124.
[12] G.F. Miller, P.M. Todd, S.U. Hegde, Designing Neural Net-
works 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. Lang-
ton, 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 Ho-
mologous 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 Ma-
son University, Fairfax, Virginia (1997).
[17] M. A. Potter, K. A. De Jong, Cooperative coevolution: An
architecture for evolving coadapted subcomponents, Evolu-
tionary Computation 8(1), 1-29 (2000).
[18] T. Praczyk, Evolving co-adapted subcomponents in Assem-
bler 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 pen-
dulum problem, Computing and Informatics 28, 2009, pp.
895-912 (2009).
[21] T. Praczyk, Forming Neural Networks by Means of Assem-
bler Encoding, Intelligent Automation and Soft Computing,
17(3), 319-331 (2011).
[22] T. Praczyk, P. Szymak, Decision System for a Team of Au-
tonomous Underwater Vehicles – Preliminary Report, Neu-
rocomputing, Neurocomputing 74(17): 3323-3334 (2011).
[23] T. Praczyk Solving the pole balancing problem by means of
assembler encoding, Journal of Intelligent and Fuzzy Sys-
tems, in press
[24] W. Siler, J. Buckley, Fuzzy Expert Systems and Fuzzy Rea-
soning, John Wiley and Sons Ltd. (2005).
[25] O. Stanley, Efficient Evolution of Neural Networks Through
Complexification, PhD thesis, Department of Computer Sci-
ence, 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 under-
water vehicle in inspection of oceanotechnical objects, Pol-
ish Neural Network Society, Artificial Intelligence and Soft
Computing, 163-168 (2006).
[28] P. Szymak, Simplified mathematical model of underwater ve-
hicle and its control system, in Polish, Pomiary, Automatyka
i Robotyka, No. 2 (2010).
[29] D. Whitley, J. Kauth, GENITOR: A different genetic algo-
rithm, In Proceedings of The Rocky Mountain Conference
on Artificial Intelligence, 118-130 (1988).
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.
Key words:
References:
[1] L. Baird III, Reinforcement Learning Through Gradient De-
scent, 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, optimiza-
tion and machine learning, Addison Wesley, Reading, Mas-
sachusetts (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, Accel-
erated Neural Evolution through Cooperatively Coevolved
Synapses, Journal of Machine Learning Research, 9, 937-965
(2008).
[7] F. Gruau, Neural network Synthesis Using Cellular Encod-
ing And The Genetic Algorithm, PhD Thesis, Ecole Normale
Superieure de Lyon (1994).
[8] H. Kitano, Designing neural networks using genetic algo-
rithms 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 Cy-
bernetics, 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 Con-
ference, (Stanford University, CA, USA, Stanford Bookstore,
1996) 117-124.
[12] G.F. Miller, P.M. Todd, S.U. Hegde, Designing Neural Net-
works 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. Lang-
ton, 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 Ho-
mologous 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 Ma-
son University, Fairfax, Virginia (1997).
[17] M. A. Potter, K. A. De Jong, Cooperative coevolution: An
architecture for evolving coadapted subcomponents, Evolu-
tionary Computation 8(1), 1-29 (2000).
[18] T. Praczyk, Evolving co-adapted subcomponents in Assem-
bler 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 pen-
dulum problem, Computing and Informatics 28, 2009, pp.
895-912 (2009).
[21] T. Praczyk, Forming Neural Networks by Means of Assem-
bler Encoding, Intelligent Automation and Soft Computing,
17(3), 319-331 (2011).
[22] T. Praczyk, P. Szymak, Decision System for a Team of Au-
tonomous Underwater Vehicles – Preliminary Report, Neu-
rocomputing, Neurocomputing 74(17): 3323-3334 (2011).
[23] T. Praczyk Solving the pole balancing problem by means of
assembler encoding, Journal of Intelligent and Fuzzy Sys-
tems, in press
[24] W. Siler, J. Buckley, Fuzzy Expert Systems and Fuzzy Rea-
soning, John Wiley and Sons Ltd. (2005).
[25] O. Stanley, Efficient Evolution of Neural Networks Through
Complexification, PhD thesis, Department of Computer Sci-
ence, 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 under-
water vehicle in inspection of oceanotechnical objects, Pol-
ish Neural Network Society, Artificial Intelligence and Soft
Computing, 163-168 (2006).
[28] P. Szymak, Simplified mathematical model of underwater ve-
hicle and its control system, in Polish, Pomiary, Automatyka
i Robotyka, No. 2 (2010).
[29] D. Whitley, J. Kauth, GENITOR: A different genetic algo-
rithm, In Proceedings of The Rocky Mountain Conference
on Artificial Intelligence, 118-130 (1988).
