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Genetic Programming Theory and Practice X

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Cover of 'Genetic Programming Theory and Practice X'

Table of Contents

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