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.