Knowledge-based artificial neural networks
Artificial Intelligence
Simulation system for real-time planning, scheduling, and control
WSC '96 Proceedings of the 28th conference on Winter simulation
Online Interactive Neuro-evolution
Neural Processing Letters
Adaptive Control Utilising Neural Swarming
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
Grounding Robotic Control with Genetic Neural Networks
Grounding Robotic Control with Genetic Neural Networks
Robust non-linear control through neuroevolution
Robust non-linear control through neuroevolution
Efficient evolution of neural network topologies
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Active guidance for a finless rocket using neuroevolution
GECCO'03 Proceedings of the 2003 international conference on Genetic and evolutionary computation: PartII
Neuroevolutionary Inventory Control in Multi-Echelon Systems
ADT '09 Proceedings of the 1st International Conference on Algorithmic Decision Theory
Evolving parameterised policies for stochastic constraint programming
CP'09 Proceedings of the 15th international conference on Principles and practice of constraint programming
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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.