Abstract
In this paper we present a new chromosome representation for evolving digital circuits. The representation is based very closely on the chip architecture of the Xilinx 6216 FPGA. We examine the effectiveness of evolving circuit functionality by using randomly chosen examples taken from the truth table. We consider the merits of a cell architecture in which functional cells alternate with routing cells and compare this with an architecture in which any cell can implement a function or be merely used for routing signals. It is noteworthy that the presence of elitism significantly improves the Genetic Algorithm performance.
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© 1998 Springer-Verlag Berlin Heidelberg
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Miller, J.F., Thomson, P. (1998). Aspects of digital evolution: Geometry and learning. 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/BFb0057604
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DOI: https://doi.org/10.1007/BFb0057604
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