How to evolve complex combinational circuits from scratch?

Created by W.Langdon from gp-bibliography.bib Revision:1.4504

  author =       "Zdenek Vasicek and Lukas Sekanina",
  title =        "How to evolve complex combinational circuits from
  booktitle =    "2014 IEEE International Conference on Evolvable
  year =         "2014",
  pages =        "133--140",
  address =      "Orlando, FL, USA",
  month =        "9-12 " # dec,
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming",
  isbn13 =       "978-1-4799-4479-8",
  DOI =          "doi:10.1109/ICES.2014.7008732",
  size =         "8 pages",
  abstract =     "One of the serious criticisms of the evolutionary
                 circuit design method is that it is not suitable for
                 the design of complex large circuits. This problem is
                 especially visible in the evolutionary design of
                 combinational circuits, such as arithmetic circuits, in
                 which a perfect response is requested for every
                 possible combination of inputs. This paper deals with a
                 new method which enables us to evolve complex circuits
                 from a randomly seeded initial population and without
                 providing any information about the circuit structure
                 to the evolutionary algorithm. The proposed solution is
                 based on an advanced approach to the evaluation of
                 candidate circuits. Every candidate circuit is
                 transformed to a corresponding binary decision diagram
                 (BDD) and its functional similarity is determined
                 against the specification given as another BDD. The
                 fitness value is the Hamming distance between the
                 output vectors of functions represented by the two
                 BDDs. It is shown in the paper that the BDD-based
                 evaluation procedure can be performed much faster than
                 evaluating all possible assignments to the inputs. It
                 also significantly increases the success rate of the
                 evolutionary design process. The method is evaluated
                 using selected benchmark circuits from the LGSynth91
                 set. For example, a correct implementation was evolved
                 for a 28-input frg1 circuit. The evolved circuit
                 contains less gates (a 57percent reduction was
                 obtained) than the result of a conventional
                 optimization conducted by ABC.",
  notes =        "Also known as \cite{7008732}",

Genetic Programming entries for Zdenek Vasicek Lukas Sekanina