http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
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.
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.
by Markus F. Brameier, Wolfgang Banzhaf.
Artificial Intelligence (incl. Robotics)