Genetic Programming Theory and Practice XVII / edited by Wolfgang Banzhaf, Erik Goodman, Leigh Sheneman, Leonardo Trujillo, Bill Worzel.

Format:

Book

Contributor:

Banzhaf, Wolfgang, 1955- editor.

Goodman, Erik, editor.

Sheneman, Leigh, editor.

Trujillo, Leonardo, editor.

Worzel, Bill, editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

Genetic and evolutionary computation series 1932-0167

Genetic and Evolutionary Computation, 1932-0167

Language:

English

Subjects (All):

Artificial intelligence.

Computational intelligence.

Algorithms.

Artificial Intelligence.

Computational Intelligence.

Algorithm Analysis and Problem Complexity.

Local Subjects:

Artificial Intelligence.

Computational Intelligence.

Algorithm Analysis and Problem Complexity.

Physical Description:

1 online resource (XXVI, 409 pages) : 142 illustrations, 112 illustrations in color.

Edition:

First edition 2020.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2020.

System Details:

text file PDF

Summary:

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. In this year's edition, the topics covered include many of the most important issues and research questions in the field, such as: opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and efficient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms.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.

Contents:

1. Characterizing the Effects of Random Subsampling on Lexicase Selection

2. It is Time for New Perspectives on How to Fight Bloatin GP

3. Explorations of the Semantic Learning Machine Neuroevolution Algorithm

4. Can Genetic Programming Perform Explainable Machine Learning for Bioinformatics?

5. Symbolic Regression by Exhaustive Search - Reducing the Search Space using Syntactical Constraints and Efficient Semantic Structure Deduplication

6. Temporal Memory Sharing in Visual Reinforcement Learning

7. The Evolution of Representations in Genetic Programming Trees

8. How Competitive is Genetic Programming in Business Data Science Applications?

9. Using Modularity Metrics as Design Features to Guide Evolution in Genetic Programming

10. Evolutionary Computation and AI Safety

11. Genetic Programming Symbolic Regression

12. Hands-on Artificial Evolution through Brain Programming

13. Comparison of Linear Genome Representations For Software Synthesis

14. Enhanced Optimization with Composite Objectives and Novelty Pulsation

15. New Pathways in Coevolutionary Computation

16. 2019 Evolutionary Algorithms Review

17. Evolving a Dota 2 Hero Bot with a Probabilistic Shared Memory Model

18. Modelling Genetic Programming as a Simple Sampling Algorithm

19. An Evolutionary System for Better Automatic Software Repair

Index.

Other Format:

Printed edition:

ISBN:

978-3-030-39958-0

9783030399580

Access Restriction:

Restricted for use by site license.