Genetic Programming Theory and Practice XII

Genetic Programming Theory and Practice XII

ISBN-10:
331916029X
ISBN-13:
9783319160290
Pub. Date:
06/04/2015
Publisher:
Springer International Publishing
ISBN-10:
331916029X
ISBN-13:
9783319160290
Pub. Date:
06/04/2015
Publisher:
Springer International Publishing
Genetic Programming Theory and Practice XII

Genetic Programming Theory and Practice XII

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Overview

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.

Product Details

ISBN-13: 9783319160290
Publisher: Springer International Publishing
Publication date: 06/04/2015
Series: Genetic and Evolutionary Computation
Edition description: 2015
Pages: 182
Product dimensions: 6.10(w) x 9.25(h) x 0.02(d)

Table of Contents

Application of Machine-Learing Methods to Understand Gene Expression Regulation.- Identification of Novel Genetic Models of Glaucoma using the "Emergent" Genetic Programming-Based Artificial Intelligence System.- Inheritable Epigenetics in Genetic Programming.- SKGP: The Way of the Combinator.- Sequential Symbolic Regression with Genetic Programming.- Sliding Window Symbolic Regression for Detecting Changes of System Dynamics.- Extremely Accurate Symbolic Regression for Large Feature Problems.- How to Exploit Alignment in the Error Space: Two Different GP Models.- Analyzing a Decade of Human-Competitive ("HUMIE") Winners: What Can We Learn?.- Tackling the Boolean Multiplexer Function Using a Highly Distributed Genetic Programming System.
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