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Engineering industry controllers using neuroevolution

Published online by Cambridge University Press:  22 July 2005

NABIL M. HEWAHI
NABIL M. HEWAHI
Affiliation:
Computer Science Department, Islamic University of Gaza, Gaza, Palestine
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Abstract

Neuroevolution, or evolving neural networks with evolution algorithms such as genetic algorithms, is becoming one of the hottest areas in hybrid systems research. One of the areas that become under research using neuroevolutions is the controllers. In this paper, we shall present two engineering controllers based on neuroevolutions techniques. One of the controllers is used to monitor the temperature and humidity in an industry. This controller is having a linear behavior. The second controller is concerned with scheduling parts in queues in an industry. The scheduling controller is having a nonlinear behavior. The results obtained by the proposed controllers based on neuroevolution are compared with results obtained by traditional methods such as neural networks with backpropagation and ordinary simulation for the controller. The results show that the neuroevolution approaches outperform the results obtained by other methods.

Keywords

Control SystemsNeuroevolutionReal-Time Systems
Type
PRACTICUM PAPER
Information
AI EDAM , Volume 19 , Issue 1 , February 2005 , pp. 49 - 57
Copyright
2005 Cambridge University Press

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References

REFERENCES

Agogino, A., Stanley, K., & Miikkulainen, R. (2000). Online interactive neuro-evolution. Neural Processing Letters 11, 29–37.CrossRefGoogle Scholar
Conradie, A., Miikkulainen, R., & Aldrich, C. (2002). Adaptive control utilizing neural swarming. Proc. 2002 Genetic & Evolutionary Computation Conf. (GECC-2002).
Drake, G. & Smith, J. (1996). Simulation system for real-time planning, scheduling and control. Proc. 28th Conf. Winter Simulation, pp. 1083–1090.CrossRef
Fan, J., Lau, R., & Miikkulainen, R. (2003). Utilizing domain knowledge in neuroevolution. Proc. Twentieth Int. Conf. Machine Learning (ICML-03).
Florian, R. (2004). Evolution of Alternate Object Pushing in a Simulated Embodied Agent: Preliminary Report. Romania: Center for Cognitive and Neural Studies (Coneural).
Gomez, F. (2003). Robust non-linear control through neuroevolution. PhD Thesis. University of Texas at Austin.
Gomez, F. & Miikkulainen, R. (2003). Active guidance for a fitness rocket using neuroevolution. Proc. Genetic Evolutionary Computation Conf. (GECC-03).CrossRef
Gomez, F. & Miikkulainen, R. (2004). Transfer of neuroevolved controllers in unstable domains. Proc. Genetic Evolutionary Computation Conf. (GECCO 2004).CrossRef
Law, D. & Miikkulainen, R. (1994). Grounding robotic control with genetic neural networks. Technical Report AI-94-223. Austin, TX: University of Texas at Austin, Department of of Computer Sciences.
Nolfi, S. & Floreano, D. (2000). Evolutionary Robotics. Cambridge: MIT Press.
Nolfi, S. & Parisi, D. (2002). Evolution of artificial neural networks. In Handbook of Brain Theory and Neural Networks (Arbib, M., Ed.), 2nd ed., pp. 418–421. Cambridge: MIT Press.
Stanley, K. & Miikkulainen, R. (2002). Efficient evolution of neural network topologies. Proc. 2002 Congr. Evolutionary Computation (CEC'02).CrossRef
Towell, G. & Shavlik, J. (1994). Knowledge based artificial neural networks. Artificial Intelligence 70, 119–165.CrossRefGoogle Scholar
Zhou, Z. & Chen, S. (2003). Evolving fault-tolerant neural networks. Neural Computing and Applications 11(3–4), 156–160.CrossRefGoogle Scholar