Advances in genetic programming. [Voume 1]
- edited by Kenneth E. Kinnear, Jr.
- Cambridge, Massachusetts : The MIT Press, 
- Copyright notice
- Physical description
- 1 online resource (ix, 476 pages) : illustrations
- Complex adaptive systems.
- Includes bibliographical references and indexes.
- 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.
- Publisher's summary
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 manu 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.
(source: Nielsen Book Data)
- Publisher's 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. 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.
(source: Nielsen Book Data)
- Genetic programming (Computer science)
- Programmation génétique (Informatique)
- COMPUTERS > Enterprise Applications > Business Intelligence Tools.
- COMPUTERS > Intelligence (AI) & Semantics.
- Computadores (software)
- COMPUTER SCIENCE/Artificial Intelligence
- COMPUTER SCIENCE/Machine Learning & Neural Networks
- Publication date
- Copyright date
- Complex adaptive systems
- "A Bradford book."
- 0585048444 (electronic bk.)
- 9780585048444 (electronic bk.)
- 9780262277181 (ebook)
- 0262277182 (ebook)
- 9780262111881 (hardcover)
- 0262111888 (hardcover)
- 9780262515535 (paperback)
- 0262515539 (paperback)
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