Abstract
This paper focuses on characteristics and applications of evolvable hardware (EHW) to space systems. The motivation for looking at EHW originates in the need for more autonomous adaptive space systems. The idea of evolvable hardware becomes attractive for long missions when the hardware looses optimality, and uploading new software only partly alleviates the problem if the computing hardware becomes obsolete or the sensing hardware faces needs outside original design specifications. The paper reports the first intrinsic evolution on an analog ASIC (a custom analog neural chip), suggests evolution of dynamical systems in state-space representations, and demonstrates evolution of compression algorithms with results better than the best-known compression algorithms.
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De Garis, H. “Evolvable Hardware: Genetic Programming of a Darwin Machine”. Int. Conf. on Artificial Neural Networks and Genetic Algorithms, Innsbruck, Austria, Springer Verlag, 1993
Grimbley, J. B. Automatic Analogue Network Synthesis using Genetic Algorithms, 1st IEE/IEEE Conf: Genetic Algorithms in Engineering Systems, UK, 1995
Koza, J., Bennett III, F. H., Lohn J., Dunlap, F., Keane M. A., and Andre, D. “Automated Synthesis of Computational Circuits Using Genetic Programming”. In Proc. of Second Annual Genetic Programming Conference, Stanford July 13–16,1997
Hemmi, H., Hikage, T. and Shimohara, K. AdAM: A Hardware Evolutionary System, In Proc. of ICEC, (193–196), 1997
Thompson, A. Silicon Evolution. In: Proceedings of Genetic Programming 1996 (GP96), J.R. Koza et al. (Eds), pages 444–452, MIT Press 1996
Higuchi, T., Murakawa, M., Iwata, M., Kajitani, I., Liu, W. and Salami, M., “Evolvable Hardware at Function Level.” In Proc. of ICEC, (187–192), 1997
Duong, T. A. et al., “Learning in neural networks: VLSI implementation strategies,” In: Fuzzy Logic and Neural Network Handbook, Ed: C.H. Chen, McGraw-Hill, 1995
Eberhardt, S. et al, “Analog VLSI Neural Networks: Implementation Issues and Examples in Optimization and Supervised Learning,” IEEE Trans. Indust. Electron. v39 (6):p. 552–564, Dec. 1992.
Baluja. I. Genetic Algorithms and Explicit Search Statistics. In Advances in Neural Information Processing Systems 9. Proceedings of the 1996 Conference. 1997. p. 319–25
Stoica, A. On hardware evolvability and levels of granularity. Proc. of the International Conference “Intelligent Systems and Semiotics 97: A Learning Perspective, NIST, Gaithersburg, MD, Sept. 22–25, 1997
Hayworth, K., The “Modeling Clay” approach to bio-inspired electronic hardware, To appear in Proc. ICES98, 1998.
Horowitz, P., Winfield, H.: The Art of Electronics 2nd ed Cambridge Univ. Press 1989
Salami, M., Murakawa, M., Higuchi, T., Data compression based on evolvable hardware, Proc. Evolvable Systems Workshop, International Joint Conference on Artificial Intelligence, 1997
Fukunaga A, Stechert A. Evolving nonlinear predictive models for lossless image compression with genetic programming. To appear in Proceedings of 3rd Annual Genetic Programming Conference (GP-98), Madison, Wisconsin USA, July 22–25, 1998
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© 1998 Springer-Verlag Berlin Heidelberg
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Stoica, A., Fukunaga, A., Hayworth, K., Salazar-Lazaro, C. (1998). Evolvable hardware for space applications. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds) Evolvable Systems: From Biology to Hardware. ICES 1998. Lecture Notes in Computer Science, vol 1478. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0057618
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DOI: https://doi.org/10.1007/BFb0057618
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