Seeing the light: artificial evolution, real vision
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
On the evolution of multicellularity and eusociality
Artificial Life
Artificial Life
Symbiogenesis in learning clasifier systems
Artificial Life
Proceedings of the Third European Conference on Advances in Artificial Life
Evolving teamwork and role-allocation with real robots
ICAL 2003 Proceedings of the eighth international conference on Artificial life
A learning system based on genetic adaptive algorithms
A learning system based on genetic adaptive algorithms
Artificial Life
On Dynamical Genetic Programming: Random Boolean Networks in Learning Classifier Systems
EuroGP '09 Proceedings of the 12th European Conference on Genetic Programming
Artificial symbiogenesis and differing reproduction rates
Artificial Life
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
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The Species Adaptation Genetic Algorithm (SAGA) was introduced to facilitate the open-ended evolution of artificial systems. The approach enables genotypes to increase in length through appropriate mutation operators and has been successfully exploited in the production of artificial neural networks in particular. Most recently, this has been undertaken within coevolutionary or multi-agent scenarios. This paper uses an abstract model of coevolution to examine the behaviour of SAGA on fitness landscapes which are coupled to those of other evolving entities to varying degrees. Results indicate that the basic dynamics of SAGA remain unchanged but that the rate of genome growth is affected by the degree of coevolutionary interdependence between the entities.