Genetic Programming Theory and Practice XIII

1. Evolving Simple Symbolic Regression Models by Multi-Objective Genetic Programming
Michael Kommenda, Gabriel Kronberger, Michael Affenzeller, Stephan M. Winkler, Bogdan Burlacu

2. Learning Heuristics for Mining RNA Sequence-Structure Motifs
Achiya Elyasaf, Pavel Vaks, Nimrod Milo, Moshe Sipper, Michal Ziv-Ukelson

3. Kaizen Programming for Feature Construction for Classification
Vinícius Veloso Melo, Wolfgang Banzhaf

4. GP As If You Meant It: An Exercise for Mindful Practice
William A. Tozier

5. nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star
Babak Hodjat, Hormoz Shahrzad

6. Highly Accurate Symbolic Regression with Noisy Training Data
Michael F. Korns

7. Using Genetic Programming for Data Science: Lessons Learned
Steven Gustafson, Ram Narasimhan, Ravi Palla, Aisha Yousuf

8. The Evolution of Everything (EvE) and Genetic Programming
W. P. Worzel

9. Lexicase Selection for Program Synthesis: A Diversity Analysis
Thomas Helmuth, Nicholas Freitag McPhee, Lee Spector

10. Behavioral Program Synthesis: Insights and Prospects
Krzysztof Krawiec, Jerry Swan, Una-May O’Reilly

11. Using Graph Databases to Explore the Dynamics of Genetic Programming Runs
Nicholas Freitag McPhee, David Donatucci, Thomas Helmuth

12. Predicting Product Choice with Symbolic Regression and Classification
Philip Truscott, Michael F. Korns

13. Multiclass Classification Through Multidimensional Clustering
Sara Silva, Luis Muñoz, Leonardo Trujillo, Vijay Ingalalli, Mauro Castelli, Leonardo Vanneschi

14. Prime-Time: Symbolic Regression Takes Its Place in the Real World
Sean Stijven, Ekaterina Vladislavleva, Arthur Kordon, Lander Willem, Mark E. Kotanchek

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