Evolving common LISP programs in a linear-genotype evolutionary computation system

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

  author =       "Jamie Cullen",
  title =        "Evolving common LISP programs in a linear-genotype
                 evolutionary computation system",
  booktitle =    "GEC '09: Proceedings of the first ACM/SIGEVO Summit on
                 Genetic and Evolutionary Computation",
  year =         "2009",
  editor =       "Lihong Xu and Erik D. Goodman and Guoliang Chen and 
                 Darrell Whitley and Yongsheng Ding",
  bibsource =    "DBLP, http://dblp.uni-trier.de",
  pages =        "75--80",
  address =      "Shanghai, China",
  organisation = "SigEvo",
  DOI =          "doi:10.1145/1543834.1543846",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  month =        jun # " 12-14",
  isbn13 =       "978-1-60558-326-6",
  keywords =     "genetic algorithms, genetic programming",
  abstract =     "Evolutionary Meta Programming (EMP) is an approach to
                 Evolutionary Computation, which allows freedom of
                 programming language choice in the evolved programs, as
                 well as the ready use of external tools and
                 testbenches, with which to perform fitness evaluation.
                 The current implementation of EMP uses a linear
                 genotype in a manner similar to Grammatical Evolution
                 (GE). In contrast, traditional Genetic Programming (GP)
                 typically uses a subset of the LISP programming
                 language to represent target programs in a tree-based
                 structure. The ability of EMP to leverage external
                 tools and arbitrary languages enables the rapid
                 prototyping of possibly novel approaches to
                 Evolutionary Computation. One such experiment is
                 presented herein: The evolution of Common LISP language
                 constructs using a linear genotype and associated
                 grammar, and evaluation using a real external LISP
                 interpreter. An exploratory study is performed with
                 three classic problems: Symbolic Regression, Ant Trail,
                 and Towers of Hanoi. Solutions to these problems were
                 evolved in both Common LISP and ANSI C versions, and
                 runtime and performance results collected. Present
                 results are relatively unintuitive, when compared to
                 conventional programming wisdom, with some problems
                 apparently favoring a paradigm not traditionally suited
                 to them in a non-evolutionary programming setting.",
  notes =        "Also known as \cite{DBLP:conf/gecco/Cullen09} part of

Genetic Programming entries for Jamie Cullen