Using Genetic Algorithms for Optimizing Algorithmic Control System of Biomimetic Underwater Vehicle
Praczyk Tomasz Received: 24 November 2015
Institute of Naval Weapon, Polish Naval Academy
81-103 Gdynia, ul. Śmidowicza 69
E-mail: t.praczyk@amw.gdynia.pl
Received:
Received: 24 November 2015; revised: 15 December 2015; accepted: 16 December 2015; published online: 29 December 2015
DOI: 10.12921/cmst.2015.21.04.009
Abstract:
Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with a specialized control system. The main task of the system is to move the vehicle along a path with collision avoidance. Regardless of the logic embedded in the system, i.e. whether it works as a neural network, fuzzy, expert, or algorithmic system or even as a hybrid of all the mentioned solutions, it is always parameterized and values of the system parameters affect its effectiveness. The paper reports the experiments whose goal was to optimize an algorithmic control system of a biomimetic autonomous underwater vehicle. To this end, three different genetic algorithms were used, i.e. a canonical genetic algorithm, a steady state genetic algorithm and a eugenic algorithm.
Key words:
References:
[1] Alden, M. and Van Kesteren, A. and Miikkulainen, R., Eu-
genic Evolution Utilizing a Domain Model, Proceedings
of the Genetic and Evolutionary Computation Conference
(GECCO-2002), 2002
[2] D. E. Goldberg, Genetic algorithms in search, optimiza-
tion and machine learning, Addison Wesley, Reading, Mas-
sachusetts, (1989)
[3] Malec M., Morawski M., Zajac
̨ J. Fish-like swimming pro-
totype of mobile underwater robot, Journal of Automation,
Mobile Robotics & Intelligent Systems, Vol. 4, No 3, 2010,
25-30
[4] Polani, D. and Miikkulainen, R., Fast Reinforcement Learn-
ing through Eugenic Neuro-Evolution, The University of
Texas at Austin, AI 99-277, 1999
[5] Polani, D. and Miikkulainen, R., Eugenic Neuro-Evolution
for Reinforcement Learning, Proceedings of the Genetic and
Evolutionary Computation Conference, 2000
[6] T. Praczyk, Using Assembler Encoding to construct Artificial
Neural Networks with a modular architecture, Polish Naval
Academy, 2011
[7] Prior, J. W., Eugenic Evolution for Combinatorial Optimiza-
tion, The University of Texas at Austin, 1998
[8] G. Syswerda, Uniform Crossover in Genetic Algorithms, Pro-
ceedings of the 3rd International Conference on genetic Al-
gorithms, 1989
[9] Szymak P., Malec M., Morawski M. Directions of develop-
ment of underwater vehicle with undulating propulsion, Pol-
ish Journal of Environmental Studies, Hard Publishing Com-
pany, 19(3), 107-110 (2010).
[10] P. Szymak, T. Praczyk, Control-oriented Model of
Biomimetic Underwater Vehicle Motion, Solid State
Phenomena 236 , 121-127 (2015).
[11] D. Whitley, A Genetic Algorithm Tutorial, Statistics and
Computing 4, 1994, 65-85, http://citeseer.ist.psu.edu
[12] http://cmtm.pg.gda.pl/systemy-techniki-glebinowe
Autonomous underwater vehicles are vehicles that are entirely or partly independent of human decisions. In order to obtain operational independence, the vehicles have to be equipped with a specialized control system. The main task of the system is to move the vehicle along a path with collision avoidance. Regardless of the logic embedded in the system, i.e. whether it works as a neural network, fuzzy, expert, or algorithmic system or even as a hybrid of all the mentioned solutions, it is always parameterized and values of the system parameters affect its effectiveness. The paper reports the experiments whose goal was to optimize an algorithmic control system of a biomimetic autonomous underwater vehicle. To this end, three different genetic algorithms were used, i.e. a canonical genetic algorithm, a steady state genetic algorithm and a eugenic algorithm.
Key words:
References:
[1] Alden, M. and Van Kesteren, A. and Miikkulainen, R., Eu-
genic Evolution Utilizing a Domain Model, Proceedings
of the Genetic and Evolutionary Computation Conference
(GECCO-2002), 2002
[2] D. E. Goldberg, Genetic algorithms in search, optimiza-
tion and machine learning, Addison Wesley, Reading, Mas-
sachusetts, (1989)
[3] Malec M., Morawski M., Zajac
̨ J. Fish-like swimming pro-
totype of mobile underwater robot, Journal of Automation,
Mobile Robotics & Intelligent Systems, Vol. 4, No 3, 2010,
25-30
[4] Polani, D. and Miikkulainen, R., Fast Reinforcement Learn-
ing through Eugenic Neuro-Evolution, The University of
Texas at Austin, AI 99-277, 1999
[5] Polani, D. and Miikkulainen, R., Eugenic Neuro-Evolution
for Reinforcement Learning, Proceedings of the Genetic and
Evolutionary Computation Conference, 2000
[6] T. Praczyk, Using Assembler Encoding to construct Artificial
Neural Networks with a modular architecture, Polish Naval
Academy, 2011
[7] Prior, J. W., Eugenic Evolution for Combinatorial Optimiza-
tion, The University of Texas at Austin, 1998
[8] G. Syswerda, Uniform Crossover in Genetic Algorithms, Pro-
ceedings of the 3rd International Conference on genetic Al-
gorithms, 1989
[9] Szymak P., Malec M., Morawski M. Directions of develop-
ment of underwater vehicle with undulating propulsion, Pol-
ish Journal of Environmental Studies, Hard Publishing Com-
pany, 19(3), 107-110 (2010).
[10] P. Szymak, T. Praczyk, Control-oriented Model of
Biomimetic Underwater Vehicle Motion, Solid State
Phenomena 236 , 121-127 (2015).
[11] D. Whitley, A Genetic Algorithm Tutorial, Statistics and
Computing 4, 1994, 65-85, http://citeseer.ist.psu.edu
[12] http://cmtm.pg.gda.pl/systemy-techniki-glebinowe
