Abstract
A coevolutionary competitive learning environment, for two antagonistic agents is presented. The agents are controlled by a new kind of computational network based oil a compartmentalised model of neurons. 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 (GP) known as Cartesian GP. 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 for 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 its a result of interaction (coevolution) between two intelligent agents. Our results show that, both agents exhibit interesting learning capabilities.
| Original language | English |
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| Title of host publication | GECCO 2007: GENETIC AND EVOLUTIONARY COMPUTATION CONFERENCE, VOL 1 AND 2 |
| Place of Publication | NEW YORK |
| Publisher | ACM |
| Pages | 269-276 |
| Number of pages | 8 |
| ISBN (Print) | 978-1-59593-697-4 |
| Publication status | Published - 2007 |
| Event | GECCO 2007 - London, England Duration: 7 Jul 2007 → 11 Jul 2007 |
Conference
| Conference | GECCO 2007 |
|---|---|
| City | London, England |
| Period | 7/07/07 → 11/07/07 |
Keywords
- Genetic Programming
- Co-evolution
- Brain
- Artificial Neural Networks
- SYNAPTIC PLASTICITY
- EVOLUTION
- NEUTRALITY
- LANDSCAPE