Evolving Exact Integer Algorithms with Genetic Programming

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

@InProceedings{Weise:2014:CEC,
  title =        "Evolving Exact Integer Algorithms with Genetic
                 Programming",
  author =       "Thomas Weise and Mingxu Wan and Ke Tang and Xin Yao",
  pages =        "1816--1823",
  booktitle =    "Proceedings of the 2014 IEEE Congress on Evolutionary
                 Computation",
  year =         "2014",
  month =        "6-11 " # jul,
  editor =       "Carlos A. {Coello Coello}",
  address =      "Beijing, China",
  ISBN =         "0-7803-8515-2",
  keywords =     "genetic algorithms, Genetic programming,
                 Representation and operators",
  DOI =          "doi:10.1109/CEC.2014.6900292",
  abstract =     "The synthesis of exact integer algorithms is a hard
                 task for Genetic Programming (GP), as it exhibits
                 epistasis and deceptiveness. Most existing studies in
                 this domain only target few and simple problems or test
                 a small set of different representations. In this
                 paper, we present the (to the best of our knowledge)
                 largest study on this domain to date. We first propose
                 a novel benchmark suite of 20 non-trivial problems with
                 a variety of different features. We then test two
                 approaches to reduce the impact of the negative
                 features: (a) a new nested form of Transactional Memory
                 (TM) to reduce epistatic effects by allowing
                 instructions in the program code to be permutated with
                 less impact on the program behaviour and (b) our
                 recently published Frequency Fitness Assignment method
                 (FFA) to reduce the chance of premature convergence on
                 deceptive problems. In a full-factorial experiment with
                 six different loop instructions, TM, and FFA, we find
                 that GP is able to solve all benchmark problems,
                 although not all of them with a high success rate.
                 Several interesting algorithms are discovered. FFA has
                 a tremendous positive impact while TM turns out not to
                 be useful.",
  notes =        "WCCI2014",
}

Genetic Programming entries for Thomas Weise Mingxu Wan Ke Tang Xin Yao

Citations