Evolutionary selection of features for neural sleep/wake discrimination
Journal of Artificial Evolution and Applications - Special issue on artificial evolution methods in the biological and biomedical sciences
HyperNEAT controlled robots learn how to drive on roads in simulated environment
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
Combining Multiple Inputs in HyperNEAT Mobile Agent Controller
ICANN '09 Proceedings of the 19th International Conference on Artificial Neural Networks: Part II
NEAT in HyperNEAT substituted with genetic programming
ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms
Genetic representation and evolvability of modular neural controllers
IEEE Computational Intelligence Magazine
Evolving plastic neural networks with novelty search
Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Evolving spiking networks with variable memristors
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Evolving spiking networks with variable memristors
ACM SIGEVOlution
Evolving spiking networks with variable resistive memories
Evolutionary Computation
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Neuromodulation is thought to be one of the underlying principles of learning and memory in biological neural networks. Recent experiments have shown that neuroevolutionary methods benefit from neuromodulation in simple grid-world problems. In this paper we investigate the performance of a neuroevolutionary method applied to a more realistic robotic task. While confirming the favorable effect of neuromodulatory structures, our results indicate that the evolution of such architectures requires a mechanism which allows for selective modular targetting of the neuromodulatory connections.