This book, written by the foremost international researchers and practitioners of genetic programming (GP), explores 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 ?eld, such as opportune application domains for GP-based methods, game playing and co-evolutionary search, symbolic regression and ef cient learning strategies, encodings and representations for GP, schema theorems, and new selection mechanisms. The book 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.
Chapter
1. Finding Simple Solutions to Multi-Task Visual Reinforcement
Learning Problems with Tangled Program Graphs.
Chapter
2. Grammar-based
Vectorial Genetic Programming for Symbolic Regression.
Chapter
3.
Grammatical Evolution Mapping for Semantically-Constrained Genetic
Programming.
Chapter
4. What can phylogenetic metrics tell us about useful
diversity in evolutionary algorithms?.
Chapter
5. An Exploration of
Exploration: Measuring the ability of lexicaseselection to find obscure
pathways to optimality.
Chapter
6. Feature Discovery with Deep Learning
Algebra Networks.
Chapter
7. Back To The Future Revisiting OrdinalGP &
Trustable Models After a Decade.
Chapter
8. Fitness First.
Chapter
9.
Designing Multiple ANNs with Evolutionary Development: Activity Dependence.-
Chapter
10. Evolving and Analyzing modularity with GLEAM (Genetic Learning by
Extraction and Absorption of Modules).
Chapter
11. Evolution of the
Semiconductor Industry, and the Start of X Law.
Wolfgang Banzhaf is a professor in the Department of Computer Science and Engineering at Michigan State University.
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