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: evolutionary constraints, relaxation of selection
mechanisms, diversity preservation strategies, flexing fitness
evaluation, evolution in dynamic environments, multi-objective and
multi-modal selection, foundations of evolvability, evolvable and
adaptive evolutionary operators, foundation of injecting expert
knowledge in evolutionary search, analysis of problem difficulty and
required GP algorithm complexity, foundations in running GP on the
cloud - communication, cooperation, flexible implementation, and
ensemble methods. Additional focal points for GP symbolic regression
are: (1) The need to guarantee convergence to solutions in the function
discovery mode; (2) Issues on model validation; (3) The need for model
analysis workflows for insight generation based on generated GP
solutions - model exploration, visualization, variable selection,
dimensionality analysis; (4) Issues in combining different types of
data. 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.