Evolutionary Algorithms for Learning Formulae in first-order Logic

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

  author =       "Philip G. K. Reiser",
  title =        "Evolutionary Algorithms for Learning Formulae in
                 first-order Logic",
  school =       "Computer Science, Aberystwyth",
  year =         "1999",
  address =      "University of Wales",
  month =        sep,
  keywords =     "genetic algorithms, genetic programming, ILP",
  URL =          "http://www.stancomb.co.uk/~prr/Papers/thesis.ps",
  URL =          "http://www.stancomb.co.uk/~prr/Papers/thesis.pdf",
  size =         "184 pages",
  abstract =     "The successful application of machine learning
                 algorithms depends on choosing the right space of
                 hypotheses. Finding this space is a challenge that
                 machine learning practitioners must regularly address.
                 Ideally, one could specify a large hypothesis space and
                 perform a complete search over it. However for most
                 interesting problems this is intractable. Many
                 algorithms make simplifying assumptions such as
                 learning rules separately and combining them.
                 Unfortunately, such approaches are prone to learning
                 suboptimal hypotheses.

                 An alternative is stochastic search which may not
                 necessarily return optimal hypotheses, but can provide
                 a way of rapidly locating good ones and is more robust
                 than greedy search strategies. This thesis concerns the
                 use of evolutionary algorithms to perform stochastic
                 search for formulae in (a subset of) first order logic.
                 Evolution is viewed as a complex deductive-inductive
                 process that conjectures new formulae and compares them
                 against one another based on their logical consequences
                 to determine their suitability as a solution.

                 Two algorithms were developed that comprised of two
                 competing strategies: global reliability and local
                 refinement. The first algorithm learns control
                 strategies for discrete-time dynamical systems.
                 Empirical studies revealed that although simple
                 strategies could be learned, the evolutionary algorithm
                 conjectured many formulae that are syntactically
                 incorrect or semantically in valid. As a result, search
                 was unnecessarily inefficient. By restricting
                 consideration to the problem of classification (or
                 concept learning) an explicit logical setting can be
                 adopted that defines the valid formulae. The second
                 algorithm, referred to as an `evolutionary wrapper', is
                 a hybrid inductive logic programming (ILP) evolutionary
                 algorithm. It can construct only classifiers that are
                 consistent with the logical setting. Empirical
                 investigations revealed that for some problems
                 considerable increases in predictive accuracy can be
                 achieved over ILP alone.",
  notes =        "thesis.pdf does not work with xpdf but seems fine with

Genetic Programming entries for Philip G K Reiser