Brian Goldman – författare

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. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic RegressionHybrid Structural and Behavioral Diversity Methods in GPMulti-Population Competitive Coevolution for Anticipation of Tax EvasionEvolving Artificial General Intelligence for Video Game ControllersA Detailed Analysis of a PushGP RunLinear Genomes for Structured ProgramsNeutrality, Robustness, and Evolvability in GPLocal Search in GPPRETSL: Distributed Probabilistic Rule Evolution for Time-Series ClassificationRelational Structure in Program Synthesis Problems with Analogical ReasoningAn Evolutionary Algorithm for Big Data Multi-Class Classification ProblemsA Generic Framework for Building Dispersion Operators in the Semantic SpaceAssisting Asset Model Development with Evolutionary AugmentationBuilding Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool 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. Chapters in this volume include: Similarity-based Analysis of Population Dynamics in GP Performing Symbolic RegressionHybrid Structural and Behavioral Diversity Methods in GPMulti-Population Competitive Coevolution for Anticipation of Tax EvasionEvolving Artificial General Intelligence for Video Game ControllersA Detailed Analysis of a PushGP RunLinear Genomes for Structured ProgramsNeutrality, Robustness, and Evolvability in GPLocal Search in GPPRETSL: Distributed Probabilistic Rule Evolution for Time-Series ClassificationRelational Structure in Program Synthesis Problems with Analogical ReasoningAn Evolutionary Algorithm for Big Data Multi-Class Classification ProblemsA Generic Framework for Building Dispersion Operators in the Semantic SpaceAssisting Asset Model Development with Evolutionary AugmentationBuilding Blocks of Machine Learning Pipelines for Initialization of a Data Science Automation Tool 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.