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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. 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.
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.
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: gene expression regulation,
novel genetic models for glaucoma, inheritable epigenetics,
combinators in genetic programming, sequential symbolic regression,
system dynamics, sliding window symbolic regression, large feature
problems, alignment in the error space, HUMIE winners, Boolean
multiplexer function, and highly distributed genetic programming
systems. Application areas include chemical process control,
circuit design, financial data mining and bioinformatics. 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.
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.
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.
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: gene expression regulation,
novel genetic models for glaucoma, inheritable epigenetics,
combinators in genetic programming, sequential symbolic regression,
system dynamics, sliding window symbolic regression, large feature
problems, alignment in the error space, HUMIE winners, Boolean
multiplexer function, and highly distributed genetic programming
systems. Application areas include chemical process control,
circuit design, financial data mining and bioinformatics. 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.
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