Practical and scalable evolution of digital circuits

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  author =       "A. P. Shanthi and Ranjani Parthasarathi",
  title =        "Practical and scalable evolution of digital circuits",
  journal =      "Applied Soft Computing",
  year =         "2009",
  volume =       "9",
  number =       "2",
  pages =        "618--624",
  month =        mar,
  keywords =     "genetic algorithms, genetic programming, Cartesian
                 Genetic Programming, Evolvable hardware, EHW,
                 Scalability, Digital circuits",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2008.08.004",
  URL =          "",
  abstract =     "This paper addresses the scalability problem prevalent
                 in the evolutionary design of digital circuits and
                 shows that Evolvable Hardware (EHW) can indeed be
                 considered as a viable alternative design methodology
                 for large and complex circuits. Despite the effort by
                 the EHW community to overcome the scalability problems
                 using both direct mapped techniques and developmental
                 approaches, so far only small circuits have been
                 evolved. This paper shows that, by partitioning a
                 digital circuit and making use of a modular
                 developmental approach, namely, the Modular
                 Developmental Cartesian Genetic Programming (MDCGP)
                 technique, it is indeed possible to evolve large
                 circuits. As a proof of concept, a 5 x 5 multiplier is
                 evolved for partition sizes of 32 and 64. It is shown
                 that compared to the direct evolution technique, the
                 MDCGP technique provides five times reduction in terms
                 of evolution times, 6-56percent reduction in area and
                 improved fault tolerance. The technique is readily
                 scalable and can be applied to even larger partition
                 sizes, and also to sequential circuits, thus providing
                 a promising path to evolve large and complex

Genetic Programming entries for A P Shanthi Ranjani Parthasarathi