Linear Genetic Programming (Genetic and Evolutionary Computation) - Tapa blanda

Acerca del autor

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.

De la contraportada

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 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.