Abstract
This paper is concerned with artificial evolution of neurocontrollers with adaptive synapses for autonomous mobile robots. The method consists of encoding on the genotype a set of local modification rules that synapses obey while the robot freely moves in the environment [2]. The synaptic weights are not encoded on the genotype. In the experiments presented here, a “behavior-based fitness” function gives reproductive advantage to robots that can solve a sequential task. The results show that evolutionary adaptive controllers solve the task much faster and better than evolutionary standard (non-adaptive) controllers, that the method scales up well to large architectures whereas standard controllers do not, and that evolved adaptive controllers are not trivial and cannot be reduced to a fixed-weight network.
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References
R. K Belew and M. Mitchell, editors. Adaptive Individuals in Evolving Populations: Models and Algorithms. Addison-Wesley, Redwood City, CA, 1996.
D. Floreano and F. Mondada. Evolution of plastic neurocontrollers for situated agents. In P. Maes, M. Matarić, J-A. Meyer, J. Pollack, H. Roitblat, and S. Wilson, editors, From Animals to Animats IV: Proceedings of the Fourth International Conference on Simulation of Adaptive Behavior, pages 402–410. MIT Press-Bradford Books, Cambridge, MA, 1996.
D. Floreano and F. Mondada. Evolutionary Neurocontrollers for Autonomous Mobile Robots. Neural Networks, 11:1461–1478, 1998.
D. Floreano and S. Nolfi. Adaptive behavior in competing coevolving species. In P. Husbands and I. Harvey, editors, Proceedings of the 4th European Conference on Artificial Life, Cambridge, MA, 1997. MIT Press.
D. Floreano and J. I. Urzelai. Evolution and learning in autonomous robots. In D. Mange and M. Tomassini, editors, Bio-Inspired Computing Systems. PPUR, Lausanne, 1998.
D. Flotzinger. Evolving plastic neural network controllers for autonomous robots. Msc dissertation 9580131, COGS, University of Sussex at Brighton, 1996.
I. Harvey. Is there another new factor in evolution? Evolutionary Computation, 4(3):313–329, 1997.
G. E. Hinton and S. J. Nowlan. How learning can guide evolution. Complex Systems, 1:495–502, 1987.
O. Miglino, H. H. Lund, and S. Nolfi. Evolving Mobile Robots in Simulated and Real Environments. Artificial Life, 2:417–434, 1996.
S. Nolfi and D. Floreano. Learning and evolution. Autonomous Robots, 7(1):forth-coming, 1999.
D. Parisi and S. Nolfi. How learning can influence evolution within a non-lamarckian framework. In R. K. Belew and M. Mitchell, editors, Adaptive Individuals in Evolving Populations. MIT Press, Cambridge (MA), 1996.
X. Yao. A review of evolutionary artificial neural networks. International Journal of Intelligent Systems, 4:203–222, 1993.
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Floreano, D., Urzelai, J. (1999). Evolution of Neural Controllers with Adaptive Synapses and Compact Genetic Encoding. In: Floreano, D., Nicoud, JD., Mondada, F. (eds) Advances in Artificial Life. ECAL 1999. Lecture Notes in Computer Science(), vol 1674. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-48304-7_25
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DOI: https://doi.org/10.1007/3-540-48304-7_25
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