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EBOOK
Author Brameier, Markus.
Title Linear genetic programming / Markus Brameier, Wolfgang Banzhaf.
Imprint New York : Springer, 2007.

LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
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LOCATION CALL # STATUS MESSAGE
 OHIOLINK SPRINGER EBOOKS    ONLINE  
View online
Author Brameier, Markus.
Series Genetic and evolutionary computation series
Genetic and evolutionary computation series.
Subject Genetic programming (Computer science)
Linear programming.
Alt Name Banzhaf, Wolfgang, 1955-
Description 1 online resource (xiii, 315 pages) : illustrations.
Bibliography Note Includes bibliographical references (pages 291-302) and index.
Contents About the authors -- 1. Introduction -- 1.1 Evolutionary algorithms -- 1.2 Genetic programming -- 1.3 Linear genetic programming -- 1.4 Motivation -- Part I. Fundamental analysis -- 2. Basic concepts of linear genetic programmning -- 2.1 Representation of programs -- 2.2 Execution of programs -- 2.3 Evolution of programs -- 3. Characteristics of the linear representation -- 3.1 Effective code and noneffective code -- 3.2 Structural introns and semantic introns -- 3.3 Graph interpretation -- 3.4 Analysis of program structure -- 3.5 Graph evolution -- 3.6 Summary and conclusion -- 4. A comparison with neural networks -- 4.1 Medical data mining -- 4.2 Benchmark data sets -- 4.3 Experimental setup -- 4.4 Experiments and comparison -- 4.5 Summary and conclusion -- Part II. Method design -- 5. Segment variations -- 5.1 Variation effects -- 5.2 Effective variation and evaluation -- 5.3 Variation step size -- 5.4 Causality -- 5.5 Selection of variation points -- 5.6 Characteristics of variation operators -- 5.7 Segment variation operators -- 5.8 Experimental setup -- 5.9 Experiments -- 5.10 Summary and conclusion -- 6. Instruction mutations -- 6.1 Minimum mutation step size -- 6.2 Instruction mutation operators -- 6.3 Experimental setup -- 6.4 Experiments -- 6.5 Summary and conclusion -- 7. Analysis of control parameters -- 7.1 Number of registers -- 7.2 Number of output registers -- 7.3 Rate of constants -- 7.4 Population size -- 7.5 Maximum program length -- 7.6 Initialization of linear programs -- 7.7 Constant program length -- 7.8 Summary and conclusion -- 8. A comparison with tree-based GP -- 8.1 Tree-based genetic programming -- 8.2 Benchmark problems -- 8.3 Experimental setup -- 8.4 Experiments and comparison -- 8.5 Discussion -- 8.6 Summary and conclusion -- Part III. Advanced techniques and phenomena -- 9. Control of diversity and variation step size -- 9.1 Introduction -- 9.2 Structural program distance -- 9.3 Semantic program distance -- 9.4 Control of diversity -- 9.5 Control of variation step size -- 9.6 Experimental setup -- 9.7 Experiments -- 9.8 Alternative selection criteria -- 9.9 Summary and conclusion -- 10. Code growth and neutral variations --10.1 Code growth in GP -- 10.2 Proposed causes of code growth -- 10.3 Influence of variation step size -- 10.4 Neutral variations -- 10.5 Conditional reproduction and variation -- 10.6 Experimental setup -- 10.7 Experiments -- 10.8 Control of code growth -- 10.9 Summary and conclusion -- 11. Evolution of program teams -- 11.1 Introduction -- 11.2 Team evolution -- 11.3 Combination of multiple predictors -- 11.4 Experimental setup -- 11.5 Experiments -- 11.6 Combination of multiple program outputs -- 11.7 Summary and conclusion -- Epilogue.
Summary 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.
Note Print version record.
ISBN 0387310290
9780387310299
0387310304 (electronic)
9780387310305 (electronic)
6610800251
9786610800254
OCLC # 123244648
Link Springer e-books
Additional Format Print version: Brameier, Markus. Linear genetic programming. New York : Springer, 2007 0387310290 9780387310299 (DLC) 2006920909 (OCoLC)79871927


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