Genetic programming for solving common and domain-independent generic recursive problems

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{phillips:2017:CEC,
  author =       "Tessa Phillips and Mengjie Zhang and Bing Xue",
  booktitle =    "2017 IEEE Congress on Evolutionary Computation (CEC)",
  title =        "Genetic programming for solving common and
                 domain-independent generic recursive problems",
  year =         "2017",
  editor =       "Jose A. Lozano",
  pages =        "1279--1286",
  address =      "Donostia, San Sebastian, Spain",
  publisher =    "IEEE",
  isbn13 =       "978-1-5090-4601-0",
  abstract =     "In human written computer programs, loops and
                 recursion are very important structures. Many
                 real-world applications require recursion and loops.
                 Loops and recursion can also be achieved by using
                 genetic programming (GP). There has been a lot of work
                 on GP for loops but not much on recursion. Our recent
                 initial work on GP for recursion has shown that GP can
                 be used to solve recursion problems, based on which
                 this work develops two new GP methods that can solve a
                 wider range of problems without decreasing the
                 performance. The two new methods are tested on symbolic
                 regression problems, binary classification problems,
                 and Artificial Ant problems. They are compared to
                 methods using loops, traditional GP, and the methods
                 developed in our previous work. The results show that
                 the new methods have improved the accuracy and
                 increased the range of symbolic regression problems
                 that the methods can perfectly solve, and improved the
                 performance on two of the Artificial Ant problems. The
                 new methods can also solve classification problems, and
                 have better performance than loops on many of these
                 tasks. This is the first work using recursion for
                 classification problems, and is the first design of a
                 generic recursive method for GP.",
  keywords =     "genetic algorithms, genetic programming, pattern
                 classification, program control structures, regression
                 analysis, artificial ant problems, binary
                 classification problems, domain-independent generic
                 recursive problems, human written computer programs,
                 loops, recursion problems, symbolic regression
                 problems, Computer science, Computers, Design
                 methodology, Image recognition, Libraries, Navigation",
  isbn13 =       "978-1-5090-4601-0",
  DOI =          "doi:10.1109/CEC.2017.7969452",
  month =        "5-8 " # jun,
  notes =        "IEEE Catalog Number: CFP17ICE-ART Also known as
                 \cite{7969452}",
}

Genetic Programming entries for Tessa Phillips Mengjie Zhang Bing Xue

Citations