Banzhaf / Spector / Sheneman Genetic Programming Theory and Practice XVI

1. Auflage 2019
ISBN: 978-3-030-04735-1
Verlag: Springer International Publishing
Format: PDF
Kopierschutz: 1 - PDF Watermark

E-Book, Englisch, 234 Seiten, eBook

Reihe: Genetic and Evolutionary Computation

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: evolving developmental programs for neural networks solving multiple problems, tangled program, transfer learning and outlier detection using GP, program search for machine learning pipelines in reinforcement learning, automatic programming with GP, new variants of GP, like SignalGP, variants of lexicase selection, and symbolic regression and classification techniques. The volume includes several chapters on best practices and lessons learned from hands-on experience. 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.

Zielgruppe

Research

Autoren/Hrsg.

Banzhaf, Wolfgang

Spector, Lee

Sheneman, Leigh

Inhaltsverzeichnis

1 Exploring Genetic Programming Systems with MAP-Elites.- 2 The Evolutionary Buffet Method.- 3 Emergent Policy Discovery for Visual Reinforcement Learning through Tangled Program Graphs: A Tutorial.- 4 Strong Typing, Swarm Enhancement, and Deep Learning Feature Selection in the Pursuit of Symbolic Regression-Classification.- 5 Cluster Analysis of a Symbolic Regression Search Space.- 6 What else is in an evolved name? Exploring evolvable specificity with SignalGP.- Lexicase Selection Beyond Genetic Programming.- 8 Evolving developmental programs that build neural networks for solving multiple problems.- 9 The Elephant in the Room - Towards the Application of Genetic Programming to Automatic Programming.- 10 Untapped Potential of Genetic Programming: Transfer Learning and Outlier Removal.- 11 Program Search for Machine Learning Pipelines Leveraging Symbolic Planning and Reinforcement Learning.