Genetic programming: on the programming of computers by means of natural selection
Genetic programming: on the programming of computers by means of natural selection
Automatic definition of modular neural networks
Adaptive Behavior
One jump ahead: challenging human supremacy in checkers
One jump ahead: challenging human supremacy in checkers
“Genotypes” for neural networks
The handbook of brain theory and neural networks
An Introduction to Neural Networks
An Introduction to Neural Networks
Evolving Neural Networks to Play Go
Applied Intelligence
Principles in the Evolutionary Design of Digital Circuits—Part I
Genetic Programming and Evolvable Machines
Spike-Timing Dependent Competitive Learning of Integrate-and-Fire Neurons with Active Dendrites
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Multiple Forms of Activity-Dependent Plasticity Enhance Information Transfer at a Dynamic Synapse
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Proceedings of the European Conference on Genetic Programming
Coevolution of intelligent agents using cartesian genetic programming
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Supplementing evolutionary developmental systems with abstract models of neurogenesis
Proceedings of the 9th annual conference on Genetic and evolutionary computation
Evolving an expert checkers playing program without using humanexpertise
IEEE Transactions on Evolutionary Computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
Proceedings of the 12th annual conference companion on Genetic and evolutionary computation
GECCO 2011 tutorial: cartesian genetic programming
Proceedings of the 13th annual conference companion on Genetic and evolutionary computation
GECCO 2012 tutorial: cartesian genetic programming
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
GECCO 2013 tutorial: cartesian genetic programming
Proceedings of the 15th annual conference companion on Genetic and evolutionary computation
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Biological neurons are extremely complex cells whose morphology grows and changes in response to the external environment. Yet, artificial neural networks (ANNs) have represented neurons as simple computational devices. It has been evident for a long time that ANNs have learning abilities that are insignificant compared with some of the simplest biological brains. We argue that we understand enough neuroscience to create much more sophisticated models. In this paper, we report on our attempts to do this.We identify and evolve seven programs that together represents a neuron which grows post evolution into a complete 'neurological' system. The network that occurs by running the programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change. We have evaluated the capability of these networks for playing the game of checkers. Our method has no board evaluation function, no explicit learning rules and no human expertise at playing checkers is used. The learning abilities of these networks are encoded at a genetic level rather than at the phenotype level of neural connections.