Contributions

  • Julian Miller (Editor) - Contributor
  • Marco Tomassini (Editor) - Contributor
  • Pier Luca Lanzi (Editor) - Contributor
  • Conor Ryan (Editor) - Contributor
  • Andrea G.B. Tettamanzi (Editor) - Contributor
and 1 more
  • William B. Langdon (Editor) - Contributor

Publication

2001-05-11 - Springer

Language

English

Word Count

96,000 words, Guess

Page Count

384 pages

Physical Format

Paperback

Identifiers

and 4 more

Classifications

  • LCCQA76.623 .E94 2001
  • LCCQA75.5-76.95

Description

Genetic Programming: 4th European Conference, EuroGP 2001 Lake Como, Italy, April 18–20, 2001 Proceedings<br />Author: Julian Miller, Marco Tomassini, Pier Luca Lanzi, Conor Ryan, Andrea G. B. Tettamanzi, William B. Langdon<br /> Published by Springer Berlin Heidelberg<br /> ISBN: 978-3-540-41899-3<br /> DOI: 10.1007/3-540-45355-5<br /><br />Table of Contents:<p></p><ul><li>Heuristic Learning Based on Genetic Programming </li><li>Evolving Color Constancy for an Artificial Retina </li><li>Adaptive Genetic Programming Applied to New and Existing Simple Regression Problems </li><li>An Evolutionary Approach to Automatic Generation of VHDL Code for Low-Power Digital Filters </li><li>Studying the Influence of Communication Topology and Migration on Distributed Genetic Programming </li><li>CAGE: A Tool for Parallel Genetic Programming Applications </li><li>Ripple Crossover in Genetic Programming </li><li>Evolving Receiver Operating Characteristics for Data Fusion </li><li>An Adaptive Mapping for Developmental Genetic Programming </li><li>A Schema Theory Analysis of the Evolution of Size in Genetic Programming with Linear Representations </li><li>Exact Schema Theorems for GP with One-Point and Standard Crossover Operating on Linear Structures and Their Application to the Study of the Evolution of Size </li><li>General Schema Theory for Genetic Programming with Subtree-Swapping Crossover </li><li>Evolving Modules in Genetic Programming by Subtree Encapsulation </li><li>Evolution of Affine Transformations and Iterated Function Systems Using Hierarchical Evolution Strategy </li><li>Evolving Turing Machines for Biosequence Recognition and Analysis </li><li>Neutrality and the Evolvability of Boolean Function Landscape </li><li>Polymorphism and Genetic Programming </li><li>Programmable Smart Membranes: Using Genetic Programming to Evolve Scalable Distributed Controllers for a Novel Self-Reconfigurable Modular Robotic Application </li><li>A GP Artificial Ant for image processing: preliminary experiments with EASEA. </li><li>Feature Extraction for the k-Nearest Neighbour Classifier with Genetic Programming</li></ul>

First Sentence

Genetic programming (GP) is a fast developing field which aims at evolving a population of computer codes towards a program that solves a given problem [Koz92].

Subjects

Other Editions

  • Genetic Programming: 4th European Conference, EuroGP 2001 Lake Como, Italy, April 18-20, 2001 Proceedings (Lecture Notes in Computer Science)PaperbackSpringer2001-05-11

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