Linear Genetic Programming

This book PDF is perfect for those who love Computers genre, written by Markus F. Brameier and published by Springer Science & Business Media which was released on 25 February 2007 with total hardcover pages 323. You could read this book directly on your devices with pdf, epub and kindle format, check detail and related Linear Genetic Programming books below.

Linear Genetic Programming

Author	: Markus F. Brameier
File Size	: 49,5 Mb
Publisher	: Springer Science & Business Media
Language	: English
Release Date	: 25 February 2007
ISBN	: 9780387310305
Pages	: 323 pages

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Linear Genetic Programming by Markus F. Brameier Book PDF Summary

Linear Genetic Programming presents a variant of Genetic Programming that evolves imperative computer programs as linear sequences of instructions, in contrast to the more traditional functional expressions or syntax trees. Typical GP phenomena, such as non-effective code, neutral variations, and code growth are investigated from the perspective of linear GP. This book serves as a reference for researchers; it includes sufficient introductory material for students and newcomers to the field.

Linear Genetic Programming by Markus F. Brameier,Wolfgang Banzhaf

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Genetic Programming for Image Classification by Ying Bi,Bing Xue,Mengjie Zhang

This book offers several new GP approaches to feature learning for image classification. Image classification is an important task in computer vision and machine learning with a wide range of applications. Feature learning is a fundamental step in image classification, but it is difficult due to the high variations of

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Genetic Programming III by John R. Koza

Genetic programming (GP) is a method for getting a computer to solve a problem by telling it what needs to be done instead of how to do it. Koza, Bennett, Andre, and Keane present genetically evolved solutions to dozens of problems of design, control, classification, system identification, and computational molecular

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Genetic Algorithms and Genetic Programming in Computational Finance by Shu-Heng Chen

After a decade of development, genetic algorithms and genetic programming have become a widely accepted toolkit for computational finance. Genetic Algorithms and Genetic Programming in Computational Finance is a pioneering volume devoted entirely to a systematic and comprehensive review of this subject. Chapters cover various areas of computational finance, including

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Introduction to Evolutionary Computing by Agoston E. Eiben,J.E. Smith

The first complete overview of evolutionary computing, the collective name for a range of problem-solving techniques based on principles of biological evolution, such as natural selection and genetic inheritance. The text is aimed directly at lecturers and graduate and undergraduate students. It is also meant for those who wish to

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Genetic Programming Theory and Practice by Rick Riolo,Bill Worzel

Genetic Programming Theory and Practice explores the emerging interaction between theory and practice in the cutting-edge, machine learning method of Genetic Programming (GP). The material contained in this contributed volume was developed from a workshop at the University of Michigan's Center for the Study of Complex Systems where an international

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Machine Learning Control Taming Nonlinear Dynamics and Turbulence by Thomas Duriez,Steven L. Brunton,Bernd R. Noack

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in Chapters 1 and 2. In Chapter 3, methods of

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Genetic Programming IV by John R. Koza,Martin A. Keane,Matthew J. Streeter,William Mydlowec,Jessen Yu,Guido Lanza

Genetic Programming IV: Routine Human-Competitive Machine Intelligence presents the application of GP to a wide variety of problems involving automated synthesis of controllers, circuits, antennas, genetic networks, and metabolic pathways. The book describes fifteen instances where GP has created an entity that either infringes or duplicates the functionality of a

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