Advances in genetic programming

Author(s)

• Kinnear, Kenneth E.
• Angeline, Peter J.
• Spector, Lee

Bibliographic Information

Advances in genetic programming

edited by Kenneth E. Kinnear, Jr.

（Complex adaptive systems）（Bradford book）

MIT Press, c1994-

• [v. 1]
• v. 2
• v. 3

Note

Vol. 2: edited by Peter J. Angeline and Kenneth E. Kinnear, Jr.

Vol. 3: edited by Lee Spector ... [et al.]

Includes bibliographical references and indexes

[Vol. 1]: x, 518 p. : ill., c1994. v. 2: xv, 538 p, c1996. v. 3: ix, 476 p. : ill., c1999

Description and Table of Contents

Volume

v. 2 ISBN 9780262011587

Description

Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. Genetic programming, a form of genetic algorithm that evolves programs and program-like executable structures, is a new paradigm for developing reliable, time- and cost-effective applications. The second volume of Advances in Genetic Programming highlights many of the most recent technical advances in this increasingly popular field. The twenty-three contributions are divided into four parts: Variations on the Genetic Programming Theme; Hierarchical, Recursive, and Pruning Genetic Programs; Analysis and Implementation Issues; and New Environments for Genetic Programming. The first part extends the core concepts of genetic programming through the addition of new evolutionary techniques-adaptive and self-adaptive crossover methods, hill climbing operators, and the inclusion of introns into the representation. Creating more concise executable structures is a long-term research topic in genetic programming. The second part describes the field's most recent efforts, including the dynamic manipulation of automatically defined functions, evolving logic programs that generate recursive structures, and using minimum description length heuristics to determine when and how to prune evolving structures. The third part takes up the many implementation and analysis issues associated with evolving programs. Advanced applications of genetic programming to nontrivial real-world problems are described in the final part: remote sensing of pressure ridges in Arctic sea ice formations from satellite imagery, economic prediction through model evolution, the evolutionary development of stress and loading models for novel materials, and data mining of a large customer database to optimize responses to special offers.

Table of Contents

• Genetic programming's continued evolution, Peter J. Angeline. Part 1 Variations on the genetic programming theme: a comparative analysis of genetic programming, Una-May O'Reilly and Franz Oppacher
• evolving programmers - the co-evolution of intelligent recombination operators, Astro Teller
• extending genetic programming with recombinative guidance, Horishi Iba and Hugo de Garis
• two self-adaptive crossover operators for genetic programming, Peter J. Angeline
• explicitly defined introns and destructive crossover in genetic programming, Peter Nordin et al. Part 2 modular, recursive and pruning genetic programmes: simultaneous evolution of programmes and their control structures, Lee Spector
• classifying protein segments as transmembrane domains - using architecture-altering operations in genetic programming, John R. Koza and David Andre
• discovery of subroutines in genetic programming, Justinian P. Rosca and Dana H. Ballard
• evolving recursive programmes for tree search, Scott Brave
• evolving recursive functions for the even-parity problem using genetic programming, Man Leung Wong and Kwong Sak Leung
• adaptive fitness functions for dynamic growing/pruning of programme trees, Byoung-Tak Zhang and Heinz Muhlenbein. Part 3 Analysis and implementation issues in genetic programming: efficiently representing populations in genetic programming, Maarten Keijzer
• genetically optimizing the speed of programmes evolved to play tetris, Eric V. Siegel and Alexander D. Chaffee
• the royal tree problem, a benchmark for single and multiple population genetic programming, William F. Punch et al
• parallel genetic programming - a scalable implementation using the transputer network architcture, David Andre and John R. Koza
• massively parallel genetic programming, Hugues Juille and Jordan B. Pollack
• type inheritance in strongly typed genetic programming, Thomas D. Haynes et al
• on using syntactic constraints with genetic programming, Frederic Gruau
• data structures and genetic programming, William B. Langdon. Part 4 New environments for genetic programming: algorithm discovery using the genetic programming paradigm - extracting low-contrast curvilinear features from SAR images of Arctic ice, Jason M. Daida et al
• genetic programming learning and the cobweb model, Shu-Heng Chen and Chia-Hsuan Yeh
• evolutionary identification of macro-mechanical models, Marc Shoenauer et al
• discovering time oriented abstractions in historical data to optimize decision tree classification, Brij Masand and Gregory Piatetsky-Shapiro. Part 5 Appendices: genetic programming resources on the World-Wide Web, Patrick Tufts
• a bibliography for genetic programming, William B. Langdon.

Volume

[v. 1] ISBN 9780262111881

Description

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.

Table of Contents

• Part 1 Introduction: a perspective on the work in this book, Kenneth E. Kinnear
• introduction to genetic programming, John R. Koza. Part 2 Increasing the power of genetic programming: 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
• the Donut problem - scalability, generalization and breeding policies in genetic programming, Walter Alden Tackett and Aviram Carmi
• effects of locality in individual and population evolution, Patrik D'haeseleer and Jason Bluming
• the evolution of mental models, Astro Teller
• evolution of obstacle avoidance behaviour - using noise to promote robust solutions, Craig W. Reynolds
• pygmies and civil servants, Conor Ryan
• genetic programming using a minimum description length principle, Hitoshi Iba et al
• genetic programming in C++ - implementation issues, Mike J. Keith and Martin C. Martin
• a compiling genetic programming system that directly manipulates the machine code, Peter Nordin. Part 3 Innovative applications of genetic programming: automatic generation of programs for crawling and walking, Graham Spencer
• genetic programming for the acquisition of double auction market strategies, Martin Andrews and 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 modelling for model-based object recognition systems, Thang Nguyen and 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, Frederic Gruau.

Volume

v. 3 ISBN 9780262194235

Description

Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field. Genetic programming is a form of evolutionary computation that evolves programs and program-like executable structures for developing reliable time-and cost-effective applications. It does this by breeding programs over many generations, using the principles of natural selection, sexual recombination, and mutuation. This third volume of Advances in Genetic Programming highlights many of the recent technical advances in this increasingly popular field.

by "Nielsen BookData"

Details

• NCID: BA22496297
• ISBN:
  • 0262111888
  • 0262011581
  • 0262194236
• LCCN: 93047518
• Country Code: us
• Title Language Code: eng
• Text Language Code: eng
• Place of Publication: Cambridge, Mass.
• Pages/Volumes: v.
• Size: 24 cm
• Classification:
  • NDC8 : 007.64
  • NDC8 : 007.1
  • LCC : QA76.6
  • DC20 : 006.3
• Subject Headings:
  • LCSH : Electronic digital computers -- Programming
• Parent Bibliography ID:
  • BA14399342
  • BA00245453