Competent Program Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.4202

  author =       "Moshe Looks",
  title =        "Competent Program Evolution",
  school =       "Washington University",
  year =         "2006",
  type =         "Doctor of Science",
  address =      "St. Louis, USA",
  month =        "11 " # dec,
  keywords =     "genetic algorithms, genetic programming",
  URL =          "",
  size =         "101 pages",
  abstract =     "Heuristic optimization methods are adaptive when they
                 sample problem solutions based on knowledge of the
                 search space gathered from past sampling. Recently,
                 competent evolutionary optimization methods have been
                 developed that adapt via probabilistic modeling of the
                 search space. However, their effectiveness requires the
                 existence of a compact problem decomposition in terms
                 of prespecified solution parameters.

                 How can we use these techniques to effectively and
                 reliably solve program learning problems, given that
                 program spaces will rarely have compact decompositions?
                 One method is to manually build a problem-specific
                 representation that is more tractable than the general
                 space. But can this process be automated? My thesis is
                 that the properties of programs and program spaces can
                 be leveraged as inductive bias to reduce the burden of
                 manual representation-building, leading to competent
                 program evolution.

                 The central contributions of this dissertation are a
                 synthesis of the requirements for competent program
                 evolution, and the design of a procedure,
                 meta-optimizing semantic evolutionary search (MOSES),
                 that meets these requirements. In support of my thesis,
                 experimental results are provided to analyze and verify
                 the effectiveness of MOSES, demonstrating scalability
                 and real-world applicability.",
  notes =        "",

Genetic Programming entries for Moshe Looks