Computation Process Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4420

  author =       "Ji Lu and Tao Li",
  title =        "Computation Process Evolution",
  booktitle =    "2006 IEEE International Conference on Engineering of
                 Intelligent Systems",
  year =         "2006",
  pages =        "1--6",
  organisation = "Islamabad",
  publisher =    "IEEE",
  keywords =     "genetic algorithms, genetic programming, gene
                 expression programming",
  ISBN =         "1-4244-0456-8",
  DOI =          "doi:10.1109/ICEIS.2006.1703138",
  abstract =     "Unlike other genetic methods which are devoted to
                 optimise the input data, this paper proposes an
                 approach, CPE, aiming at finding the computation
                 process of any problem by only using a few input and
                 output data, consisting of the cases needed to be
                 satisfied and those needed to be avoided. It first
                 encodes the antibody using the method similar to that
                 of gene expression programming (GEP), a new efficient
                 technique of genetic programming (GP) with linear
                 representation. Through the gradual evolution, the
                 affinity between antibody and the non-selves become
                 more and more intense. At the same time, every time
                 after the chromosomes are mutated, the chromosomes
                 should be checked to determine whether the antibody
                 chromosome would match the selves, which are the
                 conditions that should be satisfied. Two kind of
                 experiment are examined in order to test the
                 performance of the approach. The results show that CPE
                 evolves out the data-processing processes which are
                 exactly the same as those from which the experimental
                 input data were generated, and compared with GP and GEP
                 which is currently one of the most efficient genetic
                 methods, CPE experiences shorter evolution process.
                 Most importantly, unlike previous evolutionary methods
                 that only consider increasing fitness, this approach
                 takes into account both the goal (fitness) and the
                 constraints of actual problems, which makes it possible
                 to solve complex real problems using evolutionary
  notes =        "INSPEC Accession Number: 9133110

                 Dept. of Comput. Sci., Sichuan Univ., Chengdu;",

Genetic Programming entries for Ji Lu Tao Li