Genetic approaches to learning recursive relations

Created by W.Langdon from gp-bibliography.bib Revision:1.2031

@InCollection{whigham:1995:glrr,
  author =       "P. A. Whigham and R. I. McKay",
  title =        "Genetic approaches to learning recursive relations",
  booktitle =    "Progress in Evolutionary Computation",
  publisher =    "Springer-Verlag",
  year =         "1995",
  editor =       "Xin Yao",
  volume =       "956",
  series =       "Lecture Notes in Artificial Intelligence",
  pages =        "17--27",
  publisher_address = "Heidelberg, Germany",
  keywords =     "genetic algorithms, genetic programming, Machine
                 Learning, Inductive Logic Programming",
  doi =          "doi:10.1007/3-540-60154-6_44",
  size =         "11 pages",
  abstract =     "The genetic programming (GP) paradigm is a new
                 approach to inductively forming programs that describe
                 a particular problem. The use of natural selection
                 based on a fitness function for reproduction of the
                 program population has allowed many problems to be
                 solved that require a non-fixed representation. Issues
                 of typing and language forms within the genetic
                 programming paradigm are discussed. The recursive
                 nature of many geospatial problems leads to a study of
                 learning recursive definitions in a subset of a
                 functional language. The inadequacy of GP to create
                 recursive definitions is argued, and a class of
                 problems hypothesised that are difficult for genetic
                 approaches. Operations from the field of Inductive
                 Logic Programming, such as the V and W operators, are
                 shown to have analogies with GP crossover but are able
                 to handle some recursive definitions. Applying a
                 genetic approach to ILP operators is proposed as one
                 approach to learning recursive relations.",
  notes =        "

                 p18 'the negative results...suggest that GP is _not_
                 suitable for discovering recursive definitions'. Tries
                 ILP+GP. Tries to learn LISP member function with CAR,
                 CDR, EQ, ATOM, MEMBER.Stack limit of 40 calls was
                 imposed. No solutions found (without ILP), due to
                 fitness function and halting problem? RLGG.",
  affiliation =  "University College, University of New South Wales
                 Australian Defence Force Academy Department of Computer
                 Science 2600 Canberra ACT Australia 2600 Canberra ACT
                 Australia",
}

Genetic Programming entries for Peter Alexander Whigham R I (Bob) McKay