Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Artificial intelligence: a modern approach
Artificial intelligence: a modern approach
Artificial Life
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
Neutrality and the Evolvability of Boolean Function Landscape
EuroGP '01 Proceedings of the 4th European Conference on Genetic Programming
Finding Needles in Haystacks Is Not Hard with Neutrality
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
A multi-chromosome approach to standard and embedded cartesian genetic programming
Proceedings of the 8th annual conference on Genetic and evolutionary computation
Competitive coevolution through evolutionary complexification
Journal of Artificial Intelligence Research
Developing neural structure of two agents that play checkers using cartesian genetic programming
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Coevolution of Neuro-developmental Programs That Play Checkers
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Breaking the Synaptic Dogma: Evolving a Neuro-inspired Developmental Network
SEAL '08 Proceedings of the 7th International Conference on Simulated Evolution and Learning
Evolution, development and learning using self-modifying cartesian genetic programming
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Evolution of cartesian genetic programs capable of learning
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference: Late Breaking Papers
In search of intelligent genes: the cartesian genetic programming computational neuron (CGPCN)
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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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A coevolutionary competitive learning environment for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based on a compartmentalised model of neurons. We have taken the view that the genetic basis of neurons is an important [27] and neglected aspect of previous approaches. Accordingly, we have defined a collection of chromosomes representing various aspects of the neuron: soma, dendrites and axon branches, and synaptic connections. Chromosomes are represented and evolved using a form of genetic programming known as Cartesian Genetic Programming. The network formed by running the chromosomal programs has a highly dynamic morphology in which neurons grow, and die, and neurite branches together with synaptic connections form and change in response to environmental interactions. The idea of this paper is to demonstrate the importance of the genetic transfer of learned experience and life time learning. The learning is a consequence of the complex dynamics produced as a result of interaction (coevolution) between two intelligent agents. Our results show that both agents exhibit interesting learning capabilities.