Coevolution of Neuro-developmental Programs That Play Checkers

Authors:
Gul Muhammad Khan;Julian F. Miller;David M. Halliday
Affiliations:
Electronics Department, University of York, York, UK YO10 5DD;Electronics Department, University of York, York, UK YO10 5DD;Electronics Department, University of York, York, UK YO10 5DD
Venue:
ICES '08 Proceedings of the 8th international conference on Evolvable Systems: From Biology to Hardware
Year:
2008

Citing 6
Cited 3

Principles in the Evolutionary Design of Digital Circuits—Part II

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
Cartesian Genetic Programming

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
Competitive coevolution through evolutionary complexification

Journal of Artificial Intelligence Research

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

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a method for co-evolving neuro-inspired developmental programs for playing checkers. Each player's program is represented by seven chromosomes encoding digital circuits, using a form of genetic programming, called Cartesian Genetic Programming (CGP). The neural network that occurs by running the genetic 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 situations encountered on the checkers board. The results show that, after a number of generations, by playing each other the agents play much better than those from earlier generations. Such learning abilities are encoded at a geneticlevel rather than at the phenotype level of neural connections.