Genetic Programming Theory and Practice XIII

✍ Scribed by Rick Riolo, W.P. Worzel, Mark Kotanchek, Arthur Kordon (eds.)

• Publisher: Springer International Publishing
• Year: 2016
• Tongue: English
• Leaves: 272
• Series: Genetic and Evolutionary Computation
• Edition: 1
• Category: Library

✦ Synopsis

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: multi-objective genetic programming, learning heuristics, Kaizen programming, Evolution of Everything (EvE), lexicase selection, behavioral program synthesis, symbolic regression with noisy training data, graph databases, and multidimensional clustering. It also covers several chapters on best practices and lesson learned from hands-on experience. Additional application areas include financial operations, genetic analysis, and predicting product choice. 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 Matter....Pages i-xx
Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming....Pages 1-19
Learning Heuristics for Mining RNA Sequence-Structure Motifs....Pages 21-38
Kaizen Programming for Feature Construction for Classification....Pages 39-57
GP As If You Meant It: An Exercise for Mindful Practice....Pages 59-78
nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star....Pages 79-90
Highly Accurate Symbolic Regression with Noisy Training Data....Pages 91-115
Using Genetic Programming for Data Science: Lessons Learned....Pages 117-135
The Evolution of Everything (EvE) and Genetic Programming....Pages 137-149
Lexicase Selection for Program Synthesis: A Diversity Analysis....Pages 151-167
Behavioral Program Synthesis: Insights and Prospects....Pages 169-183
Using Graph Databases to Explore the Dynamics of Genetic Programming Runs....Pages 185-201
Predicting Product Choice with Symbolic Regression and Classification....Pages 203-217
Multiclass Classification Through Multidimensional Clustering....Pages 219-239
Prime-Time: Symbolic Regression Takes Its Place in the Real World....Pages 241-260
Back Matter....Pages 261-262

✦ Subjects

Artificial Intelligence (incl. Robotics);Computational Intelligence;Algorithm Analysis and Problem Complexity;Operations Research, Management Science