%0 Journal Article %1 1082034 %A Hewahi, Nabil M. %C New York, NY, USA %D 2005 %I Cambridge University Press %J Artif. Intell. Eng. Des. Anal. Manuf. %K neuroevolution %N 1 %P 49--57 %R 10.1017/S0890060405050055 %T Engineering industry controllers using neuroevolution %U http://portal.acm.org/citation.cfm?id=1082029.1082034 %V 19 %X 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.

@article{1082034, 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.}, added-at = {2010-08-04T17:01:47.000+0200}, address = {New York, NY, USA}, author = {Hewahi, Nabil M.}, biburl = {https://www.bibsonomy.org/bibtex/2c88ec322010d50414c2ddaed2d05579e/flashbang}, description = {Engineering industry controllers using neuroevolution}, doi = {10.1017/S0890060405050055}, interhash = {243964f8895826e94fee1e437e96761f}, intrahash = {c88ec322010d50414c2ddaed2d05579e}, issn = {0890-0604}, journal = {Artif. Intell. Eng. Des. Anal. Manuf.}, keywords = {neuroevolution}, number = 1, pages = {49--57}, publisher = {Cambridge University Press}, timestamp = {2010-08-04T17:01:47.000+0200}, title = {Engineering industry controllers using neuroevolution}, url = {http://portal.acm.org/citation.cfm?id=1082029.1082034}, volume = 19, year = 2005 }