Advances in genetic programming. [Vol. 1] / edited by Kenneth E. Kinnear, Jr.

Format:

Book

Contributor:

Kinnear, Kenneth E., editor.

MITCogNet.

Series:

Complex adaptive systems.

Complex adaptive systems

Language:

English

Subjects (All):

Genetic programming (Computer science).

Physical Description:

1 online resource (ix, 476 pages) : illustrations.

Place of Publication:

Cambridge, Massachusetts : The MIT Press, [1994]

Language Note:

English

Summary:

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.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 behavior. 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.

Contents:

A perspective on the work in this book / Kenneth E. Kinnear, Jr.

Introduction to genetic programming / John R. Koza

The evolution of evolvability in genetic programming / Lee Altenberg

Genetic programming and emergent intelligence / Peter J. Angeline

Scalable learning in genetic programming using automatic function definition / John R. Koza

Alternatives in automatic function definition: a comparison of performance / Kenneth E. Kinnear, Jr.

The donut problem: scalability, generalization and breeding policies in genetic programming / Walter Alden Tackett, Aviram Carmi

Effects of locality in individual and population evolution / Patrik D'haeseleer, Jason Bluming

The evolution of mental models / Astro Teller

Evolution of obstacle avoidance behavior: using noise to promote robust solutions / Craig W. Reynolds

Pygmies and civil servants / Conor Ryan

Genetic programming using a minimum decsription length principle / Hitoshi Iba, Hugo de Garis, Taisuke Sato

Genetic programming in C++: implementation issues / Mike J. Keith, Martin C. Martin. A compiling genetic programming system that directly manipulates the machine code / Peter Nordin

Automatic generation of programs for crawling and walking / Graham Spencer

Genetic programming for the acquisition of double auction market strategies / Martin Andrews, Richard Prager

Two scientific applications of genetic programming: stack filters and non-linear equation fitting to chaotic data / Howard Oakley

The automatic generation of plans for a mobile robot via genetic programming with automatically defined functions / Simon G. Handley

Competitively evolving decision trees against fixed training cases for natural language processing / Eric V. Siegel

Cracking and co-evolving randomizers / Jan Jannink

Optimizing confidence of text classification by evolution of symbolic expressions / Brij Masand

Evolvable 3D modeling for model-based object recognition systems / Thang Nguyen, Thomas Huang. Automatically defined features: the simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them / David Andre

Genetic micro programming of neural networks / Frédéric Gruau.

Notes:

Includes bibliographical references and index.

OCLC-licensed vendor bibliographic record.

"A Bradford book."

Available through MITCogNet.

ISBN:

0-262-27718-2

0-585-04844-4

9780262277181

OCLC:

937023361