Ebook: Genetic Programming Theory and Practice X
- Tags: Artificial Intelligence (incl. Robotics), Theory of Computation, Algorithm Analysis and Problem Complexity, Programming Techniques
- Series: Genetic and Evolutionary Computation
- Year: 2013
- Publisher: Springer-Verlag New York
- Edition: 1
- Language: English
- pdf
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: 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.
Content:
Front Matter....Pages i-xxvi
Evolving SQL Queries from Examples with Developmental Genetic Programming....Pages 1-14
A Practical Platform for On-Line Genetic Programming for Robotics....Pages 15-29
Cartesian Genetic Programming for Image Processing....Pages 31-44
A New Mutation Paradigm for Genetic Programming....Pages 45-58
Introducing an Age-Varying Fitness Estimation Function....Pages 59-71
EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System....Pages 73-85
Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing....Pages 87-101
Meta-Dimensional Analysis of Phenotypes Using the Analysis Tool for Heritable and Environmental Network Associations (ATHENA): Challenges with Building Large Networks....Pages 103-115
A Baseline Symbolic Regression Algorithm....Pages 117-137
Symbolic Regression Model Comparison Approach Using Transmitted Variation....Pages 139-154
A Framework for the Empirical Analysis of Genetic Programming System Performance....Pages 155-169
More or Less? Two Approaches to Evolving Game-Playing Strategies....Pages 171-185
Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model....Pages 187-203
FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud....Pages 205-221
Representing Communication and Learning in Femtocell Pilot Power Control Algorithms....Pages 223-238
Back Matter....Pages 239-242
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.
Content:
Front Matter....Pages i-xxvi
Evolving SQL Queries from Examples with Developmental Genetic Programming....Pages 1-14
A Practical Platform for On-Line Genetic Programming for Robotics....Pages 15-29
Cartesian Genetic Programming for Image Processing....Pages 31-44
A New Mutation Paradigm for Genetic Programming....Pages 45-58
Introducing an Age-Varying Fitness Estimation Function....Pages 59-71
EC-Star: A Massive-Scale, Hub and Spoke, Distributed Genetic Programming System....Pages 73-85
Genetic Analysis of Prostate Cancer Using Computational Evolution, Pareto-Optimization and Post-processing....Pages 87-101
Meta-Dimensional Analysis of Phenotypes Using the Analysis Tool for Heritable and Environmental Network Associations (ATHENA): Challenges with Building Large Networks....Pages 103-115
A Baseline Symbolic Regression Algorithm....Pages 117-137
Symbolic Regression Model Comparison Approach Using Transmitted Variation....Pages 139-154
A Framework for the Empirical Analysis of Genetic Programming System Performance....Pages 155-169
More or Less? Two Approaches to Evolving Game-Playing Strategies....Pages 171-185
Symbolic Regression Is Not Enough: It Takes a Village to Raise a Model....Pages 187-203
FlexGP.py: Prototyping Flexibly-Scaled, Flexibly-Factored Genetic Programming for the Cloud....Pages 205-221
Representing Communication and Learning in Femtocell Pilot Power Control Algorithms....Pages 223-238
Back Matter....Pages 239-242
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