Advances in Genetic Programming

Created by W.Langdon from gp-bibliography.bib Revision:1.4524

  editor =       "Kenneth E. {Kinnear, Jr.}",
  title =        "Advances in Genetic Programming",
  publisher =    "MIT Press",
  year =         "1994",
  address =      "Cambridge, MA",
  keywords =     "genetic algorithms, genetic programming",
  broken =       "",
  URL =          "",
  URL =          "",
  abstract =     "Overview

                 There is increasing interest in genetic programming by
                 both researchers and professional software developers.
                 These twenty-two invited contributions show how a wide
                 variety of problems across disciplines can be solved
                 using this new paradigm.

                 Advances in Genetic Programming reports significant
                 results in improving the power of genetic programming,
                 presenting techniques that can be employed immediately
                 in the solution of complex problems in many areas,
                 including machine learning and the simulation of
                 autonomous behaviour. Popular languages such as C and
                 C++ are used in many of the applications and
                 experiments, illustrating how genetic programming is
                 not restricted to symbolic computing languages such as
                 LISP. Researchers interested in getting started in
                 genetic programming will find information on how to
                 begin, on what public domain code is available, and on
                 how to become part of the active genetic programming
                 community via electronic mail.

                 A major focus of the book is on improving the power of
                 genetic programming. Experimental results are presented
                 in a variety of areas, including adding memory to
                 genetic programming, using locality and {"}demes{"} to
                 maintain evolutionary diversity, avoiding the traps of
                 local optima by using coevolution, using noise to
                 increase generality, and limiting the size of evolved
                 solutions to improve generality.

                 Significant theoretical results in the understanding of
                 the processes underlying genetic programming are
                 presented, as are several results in the area of
                 automatic function definition. Performance increases
                 are demonstrated by directly evolving machine code, and
                 implementation and design issues for genetic
                 programming in C++ are discussed.",
  notes =        "Hardback 24 chapters, most have entries in this
  size =         "525 pages",

Genetic Programming entries for Kenneth E Kinnear Jr