Engineering industry controllers using neuroevolution

Artificial Intelligence for Engineering Design, Analysis and Manufacturing: AIEDAM

Volumn 19, Issue 1, 2005, Pages 49-57

  Hewahi, Nabil M ^a  

^a ISLAMIC UNIVERSITY OF GAZA   (Palestine)

Author keywords

Control Systems; Neuroevolution; Real Time Systems

Indexed keywords

COMPUTER SIMULATION; CONTROL SYSTEMS; GENETIC ALGORITHMS; LINEAR PROGRAMMING; REAL TIME SYSTEMS; SCHEDULING;

HYBRID SYSTEMS; LINEAR BEHAVIOR; NEUROEVOLUTION; TRADITIONAL METHODS;

NEURAL NETWORKS;

EID: 23944501362     PISSN: 08900604     EISSN: None     Source Type: Journal    
DOI: 10.1017/S0890060405050055     Document Type: Article

References (14)

1. Agogino, A., Stanley, K., & Miikkulainen, R. (2000). Online interactive neuro-evolution. Neural Processing Letters 11, 29-37.
2. Conradie, A., Miikkulainen, R., & Aldrich, C. (2002). Adaptive control utilizing neural swarming. Proc. 2002 Genetic & Evolutionary Computation Conf. (GECC-2002).
3. Drake, G., & Smith, J. (1996). Simulation system for real-time planning, scheduling and control. Proc. 28th Conf. Winter Simulation, pp. 1083-1090.
4. Fan, J., Lau, R., & Miikkulainen, R. (2003). Utilizing domain knowledge in neuroevolution. Proc. Twentieth Int. Conf. Machine Learning (ICML-03).
5. Florian, R. (2004). Evolution of Alternate Object Pushing in a Simulated Embodied Agent: Preliminary Report. Romania: Center for Cognitive and Neural Studies (Coneural).
6. Gomez, F. (2003). Robust non-linear control through neuroevolution. PhD Thesis. University of Texas at Austin.
7. Gomez, F., & Miikkulainen, R. (2003). Active guidance for a fitness rocket using neuroevolution. Proc. Genetic Evolutionary Computation Conf. (GECC-03).
8. Gomez, F., & Miikkulainen, R. (2004). Transfer of neuroevolved controllers in unstable domains. Proc. Genetic Evolutionary Computation Conf. (GECCO 2004).
9. 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.
10. Nolfi, S., & Floreano, D. (2000). Evolutionary Robotics. Cambridge: MIT Press.
11. 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.
12. Stanley, K., & Miikkulainen, R. (2002). Efficient evolution of neural network topologies. Proc. 2002 Congr. Evolutionary Computation (CEC'02).
13. Towell, G., & Shavlik, J. (1994). Knowledge based artificial neural networks. Artificial Intelligence 70, 119-165.
14. Zhou, Z., & Chen, S. (2003). Evolving fault-tolerant neural networks. Neural Computing and Applications 77(3-4), 156-160.