Genetic Programming Theory and Practice X

From the book reviews: "This book reflects the progress made in GP during recent years. It covers a large range of up-to-date applications and theoretical issues. All of the papers are valuable and are recommended reading for AI scientists or novices." (Svetlana Segarceanu, Computing Reviews, July, 2014)

• 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.