Abstract
In this paper, to conquer the scalability issue of evolvable hardware (EHW), we introduce a novel system-decomposition-strategy which realizes training set partition in the intrinsic evolution of a non-truth table based 32 characters classification system. The new method is expected to improve the convergence speed of the proposed evolvable system by compressing fitness value evaluation period which is often the most time-consuming part in an evolutionary algorithm (EA) run and reducing computational complexity of EA. By evolving target characters classification system in a complete FPGA-based experiment platform, this research investigates the influence of introducing partitioning training set technique to non-truth table based circuit evolution. The experimental results conclude that it is possible to evolve characters classification systems larger and faster than those evolved earlier, by employing our proposed scheme.
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References
Yao, X., Higuchi, T.: Promises and Challenges of Evolvable Hardware. IEEE Transactions on Systems, Man, and Cybernetics 29(1), 87–97 (1999)
Higuchi, T., et al.: Real-World Applications of Analog and Digital Evolvable Hardware. IEEE Transactions on Evolutionary Computation 3(3), 220–235 (1999)
Sekanina, L.: Evolutionary Design of Digital Circuits: Where Are Current Limits? In: Proc. of the First NASA/ESA Conference on Adaptive Hardware and Systems, AHS 2006, pp. 171–178. IEEE Computer Society Press, Los Alamitos (2006)
Kajitani, I., et al.: Variable Length Chromosome GA for Evolvable Hardware. In: Proc. of the 3rd International Conference on Evolutionary Computation ICEC 1996, pp. 443–447. IEEE press, Los Alamitos (1996)
Murakawa, M., et al.: Hardware Evolution at Function Level. In: Ebeling, W., Rechenberg, I., Voigt, H.-M., Schwefel, H.-P. (eds.) PPSN 1996. LNCS, vol. 1141, pp. 62–71. Springer, Heidelberg (1996)
Paredis, J.: Coevolutionary Computation. Artificial Life 2(4), 355–375 (1995)
Islas Pérez, E., et al.: Genetic Algorithms and Case-Based Reasoning as a Discovery and Learning Machine in the Optimization of Combinational Logic Circuits. In: Coello Coello, C.A., de Albornoz, Á., Sucar, L.E., Battistutti, O.C. (eds.) MICAI 2002. LNCS (LNAI), vol. 2313, pp. 128–137. Springer, Heidelberg (2002)
Torresen, J.: A Divide-and-Conquer Approach to Evolvable Hardware. In: Sipper, M., Mange, D., Pérez-Uribe, A. (eds.) ICES 1998. LNCS, vol. 1478, pp. 57–65. Springer, Heidelberg (1998)
Torresen, J.: A Scalable Approach to Evolvable Hardware. Genetic Programming and Evolvable Machines 3(3), 259–282 (2002)
Torresen, J.: Evolving Multiplier Circuits by Training Set and Training Vector Partitioning. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds.) ICES 2003. LNCS, vol. 2606, pp. 228–237. Springer, Heidelberg (2003)
Stomeo, E., Kalganova, T.: Improving EHW Performance Introducing a New Decomposition Strategy. In: Proc. of the 2004 IEEE Conference on Cybernetics and Intelligent Systems, Singapore, pp. 439–444 (2004)
Stomeo, E., et al.: Generalized Disjunction Decomposition for the Evolution of Programmable Logic Array Structures. In: Proc. of the First NASA/ESA Conference on Adaptive Hardware and Systems AHS 2006, pp. 179–185. IEEE Computer Society Press, Los Alamitos (2006)
Wang, J., et al.: Using Reconfigurable Architecture-Based Intrinsic Incremental Evolution to Evolve a Character Classification System. In: Hao, Y., Liu, J., Wang, Y.-P., Cheung, Y.-m., Yin, H., Jiao, L., Ma, J., Jiao, Y.-C. (eds.) CIS 2005. LNCS (LNAI), vol. 3801, pp. 216–223. Springer, Heidelberg (2005)
Sekanina, L.: Virtual Reconfigurable Circuits for Real-World Applications of Evolvable Hardware. In: Tyrrell, A.M., Haddow, P.C., Torresen, J. (eds.) ICES 2003. LNCS, vol. 2606, pp. 186–197. Springer, Heidelberg (2003)
Celoxica Inc.: RC1000 Hardware Reference Manual V2.3 (2001)
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Wang, J., Lee, C.H. (2006). Introducing Partitioning Training Set Strategy to Intrinsic Incremental Evolution. In: Gelbukh, A., Reyes-Garcia, C.A. (eds) MICAI 2006: Advances in Artificial Intelligence. MICAI 2006. Lecture Notes in Computer Science(), vol 4293. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11925231_26
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DOI: https://doi.org/10.1007/11925231_26
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49026-5
Online ISBN: 978-3-540-49058-6
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