Genetic Programming Theory and Practice X, 1st Edition

  • Published By:
  • ISBN-10: 1461468469
  • ISBN-13: 9781461468462
  • DDC: 006.31
  • Grade Level Range: College Freshman - College Senior
  • 242 Pages | eBook
  • Original Copyright 2013 | Published/Released June 2014
  • This publication's content originally published in print form: 2013

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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.

Table of Contents

Front Cover.
Half Title Page.
Title Page.
Copyright Page.
1: Evolving SQL Queries from Examples with Developmental Genetic Programming.
2: A Practical Platform for On-Line Genetic Programming for Robotics.
3: Cartesian Genetic Programming for Image Processing.
4: A New Mutation Paradigm for Genetic Programming.
5: Introducing an Age-Varying Fitness Estimation Function.
6: EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System.
7: Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-Processing.
8: Meta-Dimensional Analysis of Phenotypes Using the Analysis Tool for Heritable and Environmental Network Associations (ATHENA): Challenges with Building Large Networks.
9: A Baseline Symbolic Regression Algorithm.
10: Symbolic Regression Model Comparison Approach Using Transmitted Variation.
11: A Framework for the Empirical Analysis of Genetic Programming System Performance.
12: More or Less? Two Approaches to Evolving Game-Playing Strategies.
13: Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model.
14: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud.
15: Representing Communication and Learning in Femtocell Pilot Power Control Algorithms.