Incremental evolution of complex general behavior
Adaptive Behavior - Special issue on environment structure and behavior
Evolving neural networks through augmenting topologies
Evolutionary Computation
Efficient evolution of neural networks through complexification
Efficient evolution of neural networks through complexification
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Accelerated Neural Evolution through Cooperatively Coevolved Synapses
The Journal of Machine Learning Research
Real-space evolutionary annealing
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Learning in fractured problems with constructive neural network algorithms
Learning in fractured problems with constructive neural network algorithms
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Neural networks are effective tools to solve prediction, modeling, and control tasks. However, methods to train neural networks have been less successful on control problems that require the network to model intricately structured regions in state space. This paper presents neuroannealing, a method for training neural network controllers on such problems. Neuroannealing is based on evolutionary annealing, a global optimization method that leverages all available information to search for the global optimum. Because neuroannealing retains all intermediate solutions, it is able to represent the fitness landscape more accurately than traditional generational methods and so finds solutions that require greater network complexity. This hypothesis is tested on two problems with fractured state spaces. Such problems are difficult for other methods such as NEAT because they require relatively deep network topology in order to extract the relevant features of the network inputs. Neuroannealing outperforms NEAT on these problems, supporting the hypothesis. Overall, neuroannealing is a promising approach for training neural networks to solve complex practical problems.