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: evolutionary constraints, relaxation of selection mechanisms, diversity preservation strategies, flexing fitness evaluation, evolution in dynamic environments, multi-objective and multi-modal selection, foundations of evolvability, evolvable and adaptive evolutionary operators, foundation of injecting expert knowledge in evolutionary search, analysis of problem difficulty and required GP algorithm complexity, foundations in running GP on the cloud communication, cooperation, flexible implementation, and ensemble methods. Additional focal points for GP symbolic regression are: (1) The need to guarantee convergence to solutions in the function discovery mode; (2) Issues on model validation; (3) The need for model analysis workflows for insight generation based on generated GP solutions model exploration, visualization, variable selection, dimensionality analysis; (4) Issues in combining different types of data.

Readers will discover large-scale, real-world applications of GP to a variety of problem domains via in-depth presentations of the latest and most significant results.

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)

Inhalt
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.