Evolved neural networks based on cellular automata for sensory-motor controller

Neurocomputing

Volumn 69, Issue 16-18, 2006, Pages 2193-2207

  Kim, Kyung Joong ^a   Cho, Sung Bae ^a  

^a Yonsei University   (South Korea)

Author keywords

Cellular automata; Evolutionary neural network; Incremental evolution; Mobile robot control; Multi module integration

Indexed keywords

AUTOMATA THEORY; COMPUTER SIMULATION; GENETIC ALGORITHMS; INTEGRATION; LARGE SCALE SYSTEMS; MOBILE ROBOTS; OPTIMAL CONTROL SYSTEMS;

CELLULAR AUTOMATA; EVOLUTIONARY NEURAL NETWORK; INCREMENTAL EVOLUTION; MOBILE ROBOT CONTROL; MULTI-MODULE INTEGRATION;

CELLULAR NEURAL NETWORKS;

ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BEHAVIOR; GENETIC ALGORITHM; PRIORITY JOURNAL; ROBOTICS; SENSOR; SENSORIMOTOR INTEGRATION; SIGNAL TRANSDUCTION; SIMULATION;

EID: 33748432097     PISSN: 09252312     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.neucom.2005.07.013     Document Type: Article

References (27)

1. G.J. Barlow, C.K. Oh, E. Grant, Incremental evolution of autonomous controllers for unmanned aerial vehicles using multi-objective genetic programming, Proceedings of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, 2004, pp. 688-693.
2. Cho S.B., and Song G.B. Evolving CAM-brain to control a mobile robot. Appl. Math. Comput. 111 2-3 (2000) 147-162
3. de Garis H., and Korkin M. The CAM-brain machine (CBM) an FPGA based hardware tool which evolves a 1000 neuron net circuit module in seconds and updates a 75 million neuron artificial brain for real time robot control. Neurocomputing 42 1-4 (2002) 35-68
4. de Garis H., Korkin M., and Fehr G. The CAM-brain machine (CBM): an FPGA based tool for evolving a 75 million neuron artificial brain to control a lifesized kitten robot. Auton. Robot. 10 3 (2001) 235-249
5. H. de Garis, M. Korkin, P. Guttikonda, D. Cooley, Simulating the evolution of 2D pattern recognition on the CAM-Brain machine, an evolvable hardware tool for building a 75 million neuron artificial brain, Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks, vol. 6, 2000, pp. 606-609.
6. Floreano D., and Mondada F. Evolution of homing navigation in a real mobile robot. IEEE T. Syst. Man Cybern. 26 3 (1996) 396-407
7. Floreano D., and Mondada F. Evolutionary neurocontrollers for autonomous mobile robots. Neural Networks 11 7-8 (1998) 1461-1478
8. GenoByte Inc. ROBOKONEKO (Kitten Robot), 〈http://www.genobyte.com/robokoneko.html〉.
9. F. Gers, H. de Garis, M. Korkin, CoDi-1Bit: a simplified cellular automata based neural model, Proceedings of the Conference on Artificial Evolution, Nimes, France, October 1997, pp. 315-334.
10. Goldberg D.E. Genetic Algorithms in Search, Optimization, and Machine Learning (1989), Addison-Wesley, Reading, MA
11. Gomez F., and Mikkulainen R. Incremental evolution of complex general behavior. Adapt. Behav. 5 3-4 (1997) 317-342
12. K.-J. Kim, S.-B. Cho, Integration of multiple neural networks evolved on cellular automata by action selection mechanism, Seventh International Conference on Neural Information Processing vol. 2, 2000, pp. 687-692.
13. K-Team, Khepera Simulator Version 5.02 User Manual, 1999.
14. Lund H.H. Modern artificial intelligence for human-robot interaction. Proc. IEEE 92 11 (2004) 1821-1838
15. K.E. Mathias, L.D. Whitley, Initial performance comparisons for the delta coding algorithm, Proceedings of IEEE Conference on Evolutionary Computation, vol. 1, 1994, pp. 433-438.
16. Nolfi S., and Floreano D. Synthesis of autonomous robots through evolution. Trends Cog. Sci. 6 1 (2002) 31-37
17. G.B. Parker, L. Zhiyi, Evolving neural networks for hexapod leg controllers, Proceedings of IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 2, 2003, pp. 1376-1381.
18. Rusu P., Petriu E.M., Whalen T.E., Cornell A., and Spoelder H.J.W. Behavior-based neuro-fuzzy controller for mobile robot navigation. IEEE Trans. Instrum. Meas. 52 4 (2003) 1335-1340
19. Santos J., Duro R.J., Becerra J.A., Crespo J.L., and Bellas F. Considerations in the application of evolution to the generation of robot controllers. Inform. Sci. 133 3-4 (2001) 127-148
20. Smith T., Husbands P., and O'shea M. Local evolvability of statistically neutral GasNet robot controllers. Biosystems 69 2-3 (2003) 223-243
21. G.-B. Song, S.-B. Cho, Rule-based integration of multiple neural networks evolved based on cellular automata, FUZZ-IEEE'99, vol. 2, 1999 pp. 791-796.
22. Ster B. An integrated learning approach to environment modelling in mobile robot navigation. Neurocomputing 57 (2004) 215-238
23. Toffoli T., and Margolus N. Cellular Automata Machine (1987), MIT Press, Cambridge, MA
24. M. Walker, Comparing the performance of incremental evolution to direct evolution, Second International Conference on Autonomous Robots and Agents, 2004, pp. 119-124.
25. M. Walker, Mutated seeds: a performance enhancer for incremental evolution, Proceedings of the First Postgraduate Conference of the Institute of Information and Mathematical Sciences, Massey University, 2004, pp. 95-99.
26. R. Walker, O. Miglino, Simulating exploratory behavior in evolving artificial neural networks, Proceedings of the Genetic and Evolutionary Computation Conference 1999, pp. 122-1428.
27. Yao X. Evolving artificial neural networks. Proc. IEEE 87 9 (1999) 1423-1447