D. E. Moriarty and R. Miikkulainen. Evolutionary neural networks for value ordering in constraint satisfaction problems. Technical report AI94-218, Dept of Computer Sciences, The University of Texas at Austin, May 1994. Home/Search Document Details and Download
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Symbiotic Evolution of Neural Networks - Vogiatzis (1994) (Correct)
....name SANE standing for Symbiotic Adaptive Neuro Evolution. Although it has been proposed as a method for reinforcement learning and tried successfully on the creation of a neural controller which balances the pole cart system [MM94a] it has also been tested in a constraint satisfaction problem [MM94b]. The interested reader is requested to access appendix B.1 to find the whereabouts of the above mentioned papers. supervised learning task don t forget to mention the chapter. The novelty consists in the fact that each chromosome encodes a single neuron unlike most of the other neuro evolution ....
....time taken to reach the target. There are a lot of interesting control problems which could also be tried to test SANE. The aircraft autolander is a control system whose task is land an aircraft in the presence of wind disturbances [JS90] The pole balancing problem has already been considered in [MM94b], but it could be extented by having two poles on the cart or even by adding friction. There exist innumerable control problems, of great practical interest, that could be investigated using mSANE (multilayer SANE) or rSANE (recurrent SANE) Most probably the control tasks would be the natural ....
D. E. Moriarty and R. Miikkulainen. Evolutionary neural networks for value ordering in constraint satisfaction problems. Technical report AI94-218, Dept of Computer Sciences, The University of Texas at Austin, May 1994.
On-line Quality Control of Injection Molding Using Neural Networks - Garvey (1997) (Correct)
....and with data which is missing or containing noise. Typical applications of neural networks, include: pattern recognition, such as machine vision systems [22] classification [22] machine and process control [8,10] and planning and optimisation, such as constraint satisfaction problems [28]. In this work, a neural network is applied to an on line process control application involving the control of part quality in a live injection molding process. The rationale for this work is based on the superior pattern recognition capabilities of this technique and the premise that measured ....
Moriarty, D.E; Miikkulainen, R. `Evolutionary neural networks for value ordering in constraint satisfaction problems'. The University of Texas at Austin, Technical Report #AI94-218; Austin, Texas, TX, May, 1994. 72
Efficient Reinforcement Learning through Symbiotic Evolution - Moriarty, Miikkulainen (1994) (13 citations) Self-citation (Moriarty Miikkulainen) (Correct)
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Moriarty, D. E., and Miikkulainen, R. (1994a). Evolutionary neural networks for value ordering in constraint satisfaction problems. Technical Report AI94-218, Department of Computer Sciences, The University of Texas at Austin.
Efficient Reinforcement Learning through Symbiotic Evolution - Moriarty, Miikkulainen (1996) (13 citations) Self-citation (Moriarty Miikkulainen) (Correct)
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Moriarty, D. E., and Miikkulainen, R. (1994a). Evolutionary neural networks for value ordering in constraint satisfaction problems. Technical Report AI94-218, Department of Computer Sciences, The University of Texas at Austin.
Learning Sequential Decision Tasks - Moriarty, Miikkulainen (1995) Self-citation (Moriarty Miikkulainen) (Correct)
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Moriarty, D. E., and Miikkulainen, R. (1994b). Evolutionary neural networks for value ordering in constraint satisfaction problems. Technical Report AI94-218, Department of Computer Sciences, The University of Texas at Austin.
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