Markus Brameier received a PhD degree in Computer Science from the Department of Computer Science at University of Dortmund, Germany,in 2004. From 2003 to 2004 he was a postdoctoral fellow at the Stockholm Bioinformatics Center (SBC), a collaboration between Stockholm University, the Royal Institute of Technology, and Karolinska Institute, in Sweden. Currently he is Assistant Professor at the Bioinformatics Research Center (BiRC) of the University of Aarhus in Denmark. His primary research interests are in bioinformatics and genetic programming. Wolfgang Banzhaf is a professor of Computer Science at the Department of Computer Science of Memorial University of Newfoundland, Canada, and head of the department since 2003. Prior to that, he served for 10 years as Associate Professor for Applied Computer Science in the Department of Computer Science at University of Dortmund, Germany. From 1989 to 1993 he was a researcher with Mitsubishi Electric Corp., first in MELCO's Central Research Lab in Japan, then in the United States at Mitsubishi Electric Research Labs Inc., Cambridge, MA. Between 1985 and 1989 he was a postdoc in the Department of Physics, University of Stuttgart, Germany. He holds a PhD in Physics from the University of Karlruhe in Germany. His research interests are in the field of artificial evolution and self-organization studies. He has recently become more involved with bioinformatics.

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) "This book addresses a subfield of genetic programming, where solutions are represented by a sequence of instructions in an imperative programming language, such as C. Genetic programming is an iterative search algorithm based loosely on the concepts of biological evolution. ... Brameier and Banzhaf present a thorough overview that will serve as an excellent resource for graduate students, academics, and practitioners, who choose to work with linear genetic programming." (Steven Gustafson, ACM Computing Reviews, Vol. 49 (8), August, 2008)

• Fundamental Analysis.- Basic Concepts of Linear Genetic Programming.- Characteristics of the Linear Representation.- A Comparison with Neural Networks.- Method Design.- Linear Genetic Operators I — Segment Variations.- Linear Genetic Operators II — Instruction Mutations.- Analysis of Control Parameters.- A Comparison with Tree-Based Genetic Programming.- Advanced Techniques and Phenomena.- Control of Diversity and Variation Step Size.- Code Growth and Neutral Variations.- Evolution of Program Teams.- Epilogue.

Markus Brameier received a PhD degree in Computer Science from the Department of Computer Science at University of Dortmund, Germany,in 2004. From 2003 to 2004 he was a postdoctoral fellow at the Stockholm Bioinformatics Center (SBC), a collaboration between Stockholm University, the Royal Institute of Technology, and Karolinska Institute, in Sweden. Currently he is Assistant Professor at the Bioinformatics Research Center (BiRC) of the University of Aarhus in Denmark. His primary research interests are in bioinformatics and genetic programming. Wolfgang Banzhaf is a professor of Computer Science at the Department of Computer Science of Memorial University of Newfoundland, Canada, and head of the department since 2003. Prior to that, he served for 10 years as Associate Professor for Applied Computer Science in the Department of Computer Science at University of Dortmund, Germany. From 1989 to 1993 he was a researcher with Mitsubishi Electric Corp., first in MELCO's Central Research Lab in Japan, then in the United States at Mitsubishi Electric Research Labs Inc., Cambridge, MA. Between 1985 and 1989 he was a postdoc in the Department of Physics, University of Stuttgart, Germany. He holds a PhD in Physics from the University of Karlruhe in Germany. His research interests are in the field of artificial evolution and self-organization studies. He has recently become more involved with bioinformatics.

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) "This book addresses a subfield of genetic programming, where solutions are represented by a sequence of instructions in an imperative programming language, such as C. Genetic programming is an iterative search algorithm based loosely on the concepts of biological evolution. ... Brameier and Banzhaf present a thorough overview that will serve as an excellent resource for graduate students, academics, and practitioners, who choose to work with linear genetic programming." (Steven Gustafson, ACM Computing Reviews, Vol. 49 (8), August, 2008)

• Fundamental Analysis.- Basic Concepts of Linear Genetic Programming.- Characteristics of the Linear Representation.- A Comparison with Neural Networks.- Method Design.- Linear Genetic Operators I — Segment Variations.- Linear Genetic Operators II — Instruction Mutations.- Analysis of Control Parameters.- A Comparison with Tree-Based Genetic Programming.- Advanced Techniques and Phenomena.- Control of Diversity and Variation Step Size.- Code Growth and Neutral Variations.- Evolution of Program Teams.- Epilogue.