Program Induction, Complexity and Occam's Razor: The Induction of Computable Functions, Modularity and No Free Lunch Theorems

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

@Book{Woodward:book,
  author =       "John Woodward",
  title =        "Program Induction, Complexity and Occam's Razor: The
                 Induction of Computable Functions, Modularity and No
                 Free Lunch Theorems",
  publisher =    "LAP Lambert Academic Publishing",
  year =         "2010",
  month =        "29 " # jul,
  keywords =     "genetic algorithms, genetic programming",
  ISBN =         "3-8383-8934-4",
  URL =          "http://www.amazon.co.uk/Program-Induction-Complexity-Occams-Razor/dp/3838389344",
  abstract =     "Search is a broad machine learning method where
                 solutions are generated and tested. We focus on
                 evolving computable functions with genetic programming.
                 The literature reveals the complexity of programs is
                 small, indicating a limitation of current methods. No
                 Free Lunch is not valid for machine learning as simpler
                 functions are represented more frequently which is also
                 related to Occam's razor. We argue for Occam's razor,
                 not on grounds of simplicity but probability. The
                 complexity of a function depends on the primitives
                 available. If the representation can build new
                 primitives, then the complexity is independent of the
                 primitives. We give bounds on these constants and argue
                 these are the tightest. We examine representation,
                 genetic operators and fitness functions. A
                 representation which addresses a general problem is
                 fruitful as large instances can be solved by evolving
                 solutions to small instances. Different versions of a
                 fitness function are compared which take into account
                 if a program was terminated. A crossover operator is
                 introduced which acts on modules and increases the
                 probability of generating correctly terminating
                 programs.",
  notes =        "156 pages",
}

Genetic Programming entries for John R Woodward

Citations