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Can I read Linear Genetic Programming on EtoBox?
Linear Genetic Programming by Brameier M., Banzhaf W. is a nonfiction available to read on EtoBox.
What is Linear Genetic Programming about?
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 geneti
Who reads Linear Genetic Programming?
It is typically read by self-directed learners exploring a subject in depth.
Common subject areas: history, science, philosophy, social sciences.
- Author
- Brameier M., Banzhaf W.
- Publisher
- Springer Science+Business Media, LLC
- Published
- 2006
- Language
- EN
- ISBN
- 9780387312385
- Category
- nonfiction
- Subjects
- Computer Science, Engineering, Mathematics
Other editions & translations
- Evolvable Hardware (Genetic and Evolutionary Computation) (2006)
- Genetic programming theory and practice VI (2009)
- Genetic Programming Theory and Practice IV (2007)
- Evolutionary Algorithms for Solving Multi-Objective Problems (2006)
- The Design of Innovation: Lessons from and for Competent Genetic Algorithms (2006)
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