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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.
Evolving Simple Symbolic Regression Models by Multi-objective Genetic Programming.- Learning Heuristics for Mining RNA Sequence-Structure Motifs.- Kaizen Programming for Feature Construction for Classification.- GP as if You Meant It: An Exercise for Mindful Practice.- nPool: Massively Distributed Simultaneous Evolution and Cross-Validation in EC-Star.- Highly Accurate Symbolic Regression with Noisy Training Data.- Using Genetic Programming for Data Science: Lessons Learned.- The Evolution of Everything (EvE) and Genetic Programming.- Lexicase selection for program synthesis: a Diversity Analysis.- Using Graph Databases to Explore the Dynamics of Genetic Programming Runs.- Predicting Product Choice with Symbolic Regression and Classification.- Multiclass Classification Through Multidimensional Clustering.- Prime-Time: Symbolic Regression takes its place in the Real World.
Wolfgang Banzhaf, Betty H.C. Cheng, Kalyanmoy Deb, Kay E. Holekamp, Richard E. Lenski, Charles Ofria, Robert T. Pennock, William F. Punch, Danielle J. Whittaker, Betty H. C. Cheng