
Genetic Programming Theory and Practice XIV
Description
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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. Chapters in this volume include:
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Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression
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Hybrid Structural and Behavioral Diversity Methods in GP
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Multi-Population Competitive Coevolution for Anticipation of Tax Evasion
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Evolving Artificial General Intelligence for Video Game Controllers
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A Detailed Analysis of a PushGP Run
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Linear Genomes for Structured Programs
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Neutrality, Robustness, and Evolvability in GP
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Local Search in GP
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PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification
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Relational Structure in Program Synthesis Problems with Analogical Reasoning
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An Evolutionary Algorithm for Big Data Multi-Class Classification Problems
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A Generic Framework for Building Dispersion Operators in the Semantic Space
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Assisting Asset Model Development with Evolutionary Augmentation
- Building 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.
Reviews / Votes
"This highly technical book is meant for a very specialized audience: researchers in GP. The topics discussed offer interesting insight into how research in GP is evolving. . I strongly recommend this book for researchers in evolutionary computing and GP." (S. V. Nagaraj, Computing Reviews, November 12, 2020)
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