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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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
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