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E-raamat: Genetic Programming Theory and Practice XII

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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. Topics in this volume include: gene expression regulation, novel genetic models for glaucoma, inheritable epigenetics, combinators in genetic programming, sequential symbolic regression, system dynamics, sliding window symbolic regression, large feature problems, alignment in the error space, HUMIE winners, Boolean multiplexer function, and highly distributed genetic programming systems. Application areas include chemical process control, circuit design, financial data mining and bioinformatics. 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.
1 Application of Machine-Learning Methods to Understand Gene Expression Regulation
1(16)
Chao Cheng
William P. Worzel
2 Identification of Novel Genetic Models of Glaucoma Using the "EMERGENT" Genetic Programming-Based Artificial Intelligence System
17(20)
Jason H. Moore
Casey S. Greene
Douglas P. Hill
3 Inheritable Epigenetics in Genetic Programming
37(16)
William La Cava
Lee Spector
4 SKGP: The Way of the Combinator
53(20)
William P. Worzel
Duncan MacLean
5 Sequential Symbolic Regression with Genetic Programming
73(18)
Luiz Otavio V.B. Oliveira
Fernando E.B. Otero
Gisele L. Pappa
Julio Albinati
6 Sliding Window Symbolic Regression for Detecting Changes of System Dynamics
91(18)
Stephan M. Winkler
Michael Affenzeller
Gabriel Kronberger
Michael Kommenda
Bogdan Burlacu
Stefan Wagner
7 Extremely Accurate Symbolic Regression for Large Feature Problems
109(24)
Michael F. Korns
8 How to Exploit Alignment in the Error Space: Two Different GP Models
133(16)
Mauro Castelli
Leonardo Vanneschi
Sara Silva
Stefano Ruberto
9 Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn?
149(18)
Karthik Kannappan
Lee Spector
Moshe Sipper
Thomas Helmuth
William La Cava
Jake Wisdom
Omri Bernstein
10 Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System
167(14)
Hormoz Shahrzad
Babak Hodjat
Index 181