10% off all books and free delivery over £50
Buy from our bookstore and 25% of the cover price will be given to a school of your choice to buy more books. *15% of eBooks.

Genetic Programming Theory and Practice XIV

View All Editions (1)

The selected edition of this book is not available to buy right now.
Add To Wishlist
Write A Review

About

Genetic Programming Theory and Practice XIV Synopsis

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.


About This Edition

ISBN: 9783319970875
Publication date:
Author: Rick Riolo, Bill Worzel, Brian Goldman, Bill Tozier
Publisher: Springer an imprint of Springer International Publishing
Format: Hardback
Pagination: 227 pages
Series: Genetic and Evolutionary Computation
Genres: Artificial intelligence
Algorithms and data structures

Frequently asked questions