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Linear Genetic Programming

	Author(s):	Brameier M., Banzhaf W.
	Collection:
	Publisher:	Springer
	Year:	2006
	Language:	English
	Pages:	322 pages
	Size:	2.39 MB
	Extension:	PDF

[tab] [content title="Description"]Linear Genetic Programming examines 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 LGP.The text is divided into three parts, each of which details methodologies and illustrates applications. Part I introduces basic concepts of LGP 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 LGP, 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. [/content] [content title="Content"] [/content] [content title="About the author"]Wolfgang Banzhaf is Honorary Research Professor (ret.) in the Department of Computer Science of Memorial University of Newfoundland. Previously he was a professor there and served as head of department there from 2003 to 2009 and again from 2012 to 2016. In 2016 he was appointed the John R. Koza Endowed Chair in Genetic Programming at Michigan State University, East Lansing, MI, USA, and holds this position since then. Prof. Banzhaf received a "Diplom in Physik" degree in Physics (equivalent to a M.Sc.) from the Ludwig-Maximilians-University in Munich. He received his Dr.rer.nat (PhD) from the Department of Physics of the Technische Hochschule Karlsruhe, now Karlsruhe Institute of Technology (KIT). Prof. Banzhaf was postdoctoral research associate at the 1. Institute of Theoretical Physics of the University of Stuttgart, Visiting and Senior Researcher at the Central Research Lab, now the Advanced Technology R&D Center of Mitsubishi Electric Corporation in Japan and at Mitsubishi Electric Research Labs (MERL) in Cambridge, MA, USA. From 1993 to 2003 he was Associate Professor for Applied Computer Science in the Department of Computer Science of the Technical University of Dortmund. [/content] [/tab]

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