Genetic programming theory and practice XII
著者
書誌事項
Genetic programming theory and practice XII
（Genetic and evolutionary computation series）
Springer, 2015
- : pbk
大学図書館所蔵 件 / 全1件
-
該当する所蔵館はありません
- すべての絞り込み条件を解除する
注記
"This book is based on the material presented at the Twelfth Workshop on Genetic Programming Theory and Practice by the Center for the Study of Complex System at the University of Michigan in Ann Arbor on May 8th-10th, 2014"--pref
Includes bibliographical references and index
内容説明・目次
内容説明
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.
目次
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.
「Nielsen BookData」 より