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Genetic and Evolutionary Computation

By: Rick Riolo (Editor), Bill Worzel (Editor), Brian Goldman (Editor)

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Hardcover | 8 November 2018

At a Glance

Hardcover
244 Pages

Dimensions(cm)
23.5 x 15.88 x 1.91

Hardcover

$143.99

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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:

* Similarity-based Analysis of Population Dynamics in GP Performing Symbolic Regression
* Hybrid Structural and Behavioral Diversity Methods in GP
* Multi-Population Competitive Coevolution for Anticipation of Tax Evasion
* Evolving Artificial General Intelligence for Video Game Controllers
* A Detailed Analysis of a PushGP Run
* Linear Genomes for Structured Programs
* Neutrality, Robustness, and Evolvability in GP
* Local Search in GP
* PRETSL: Distributed Probabilistic Rule Evolution for Time-Series Classification
* Relational Structure in Program Synthesis Problems with Analogical Reasoning
* An Evolutionary Algorithm for Big Data Multi-Class Classification Problems
* A Generic Framework for Building Dispersion Operators in the Semantic Space
* 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.

Industry Reviews

"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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