Evolving dynamical neural networks for adaptive behavior
Adaptive Behavior
Robotic experiments in cricket phonotaxis
SAB94 Proceedings of the third international conference on Simulation of adaptive behavior : from animals to animats 3: from animals to animats 3
Robust spatial navigation in a robot inspired by chemotaxis in caenorhabditis elegans
Adaptive Behavior - Special issue on biologically inspired models of navigation
Simulated and situated models of chemical trail following in ants
Proceedings of the fifth international conference on simulation of adaptive behavior on From animals to animats 5
Evolutionary neurocontrollers for autonomous mobile robots
Neural Networks - Special issue on neural control and robotics: biology and technology
Robots, crickets and ants: models of neural control of chemotaxis and phonotaxis
Neural Networks - Special issue on neural control and robotics: biology and technology
A computational system for investigating chemotaxis-based cell aggregation
ECAL'07 Proceedings of the 9th European conference on Advances in artificial life
Autonomous mobile robot control based on white blood cell chemotaxis
CMSB'04 Proceedings of the 20 international conference on Computational Methods in Systems Biology
Design of evolvable computer languages
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Neural Networks
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We present our first results concerning the de novo evolution of motility and tactic response in systems of digital organisms. Our model organism was E. coli and the behavior of interest was gradient following, since this represents simple decision-making. Our first experiments demonstrated the evolution of a tactic response, both when provided with a hand-coded system to remember previous gradient concentrations and without this crutch where the organisms must determine how to store previous values on their own. In our second set of experiments we investigated two different rotation strategies, random and systematic, and found no significant performance difference between the two strategies. These experiments served as a stepping-stone and proof-of-concept of the infrastructure needed for our future work on the evolution of simple intelligence.