How neutral networks influence evolvability
Complexity
Proceedings of the Third European Conference on Advances in Artificial Life
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
Efficient Reinforcement Learning Through Evolving Neural Network Topologies
GECCO '02 Proceedings of the Genetic and Evolutionary Computation Conference
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We study the evolution of simple cells equipped with a genome, a rudimentary gene regulation network at transcription level and two classes of functional genes: motion effectors which allow the cell to move in response to nutrient gradients and nutrient importers required to actually feed from the environment. The model is inspired by the protist Naegleria gruberi which can switch between a feeding and dividing amoeboid state and a mobile flagellate state depending on environmental conditions. Simulation results demonstrate how selection in a variable environment affects the gene number and efficiency making the cells to rapidly switch from one expression regime to the other depending on the external conditions.