Linear Genetic Programming (Genetic and Evolutionary Computation)

Features

• Cover Type: Hard Cover with 316 pages
• Published by: Springer
• Edition: 1st Edition December 11, 2006
• Written in: English
• ISBN 10 Number: 0387310290
• ISBN 13 Number: 978-0387310299
• Book Dimensions: 9.3 x 6.2 x 1.2 inches
• Weighs: 1.4 pounds

Product Review


From the reviews:

"This is a very good book. It provides a well organized description of linear genetic programming (LGP). Much material, previously only available in research papers, has been consolidated, reorganized and extended for this book. … This book is primarily for the evolutionary computing researcher … . Post graduate students in genetic programming should read this book … . The book should be in university libraries. Considering the cost of many books these days this one is very well priced for its size and content." (Vic Ciesielski, Genetic Programming and Evolvable Machines, Vol. 9, 2008)

Product Description


Linear Genetic Programming looks at the evolution of imperative computer programs written as linear sequences of instructions. In contrast to functional expressions or syntax trees used in traditional Genetic Programming (GP), Linear Genetic Programming (LGP) employs a linear program structure as genetic material whose primary characteristics are exploited to achieve acceleration of both execution time and evolutionary progress. Online analysis and optimization of program code lead to more efficient techniques and contribute to a better understanding of the method and its parameters. In particular, the reduction of structural variation step size and non-effective variations play a key role in finding higher quality and less complex solutions. This volume investigates typical GP phenomena such as non-effective code, neutral variations and code growth from the perspective of linear GP.

The text is divided into three parts, each of which details methodologies and illustrates applications. Part I introduces basic concepts of linear GP and presents efficient algorithms for analyzing and optimizing linear genetic programs during runtime. Part II explores the design of efficient LGP methods and genetic operators inspired by the results achieved in Part I. Part III investigates more advanced techniques and phenomena, including effective step size control, diversity control, code growth, and neutral variations.

The book provides a solid introduction to the field of linear GP, as well as a more detailed, comprehensive examination of its principles and techniques. Researchers and students alike are certain to regard this text as an indispensable resource.