Cover Image

Genetic Programming Theory and Practice X

These contributions, written by the foremost international researchers and practitioners of Genetic Programming (GP), explore the synergy between theoretical and empirical results on real-world problems, producing a comprehensive view of the state of the art in GP. Topics in this volume include: evo...

Full description

Bibliographic Details
Corporate Author: SpringerLink (Online service)
Other Authors: Riolo, Rick. (Editor), Vladislavleva, Ekaterina. (Editor), Ritchie, Marylyn D. (Editor), Moore, Jason H. (Editor)
Format: Electronic
Language:English
Published: New York, NY : Springer New York : Imprint: Springer, 2013.
Series:Genetic and Evolutionary Computation,
Subjects:
Computer science.
Information theory.
Computer software.
Artificial intelligence.
Computer Science.
Artificial Intelligence (incl. Robotics).
Theory of Computation.
Algorithm Analysis and Problem Complexity.
Programming Techniques.
Online Access:https://ezaccess.library.uitm.edu.my/login?url=http://dx.doi.org/10.1007/978-1-4614-6846-2
Table of Contents:
  • Evolving SQL Queries from Examples with Developmental Genetic Programming
  • A Practical Platform for On-Line Genetic Programming for Robotics
  • Cartesian Genetic Programming for Image Processing
  • A new mutation paradigm for Genetic Programming
  • Introducing an Age-Varying Fitness Estimation Function
  • EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System
  • Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-Processing
  • Meta-dimensional analysis of phenotypes using the Analysis Tool for Heritable and Environmental Network Associations
  • A Baseline Symbolic Regression Algorithm
  • Symbolic Regression Model Comparison Approach Using Transmitted Variation
  • A Framework for the Empirical Analysis of Genetic Programming System Performance
  • More or Less? Two Approaches to Evolving Game-Playing Strategies
  • Symbolic Regression is Not Enough
  • FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud
  • Representing Communication and Learning in Femtocell Pilot Power Control Algorithms.

Similar Items