Grammatical Evolution Evolutionary Automatic Programming in an Arbitrary Language
Título:
Grammatical Evolution Evolutionary Automatic Programming in an Arbitrary Language
ISBN:
9781461504474
Autor personal:
Edición:
1st ed. 2003.
PRODUCTION_INFO:
New York, NY : Springer US : Imprint: Springer, 2003.
Descripción física:
XVI, 144 p. online resource.
Serie:
Genetic Programming ; 4
Contenido:
1. Introduction -- 1 Evolutionary Automatic Programming -- 2 Molecular Biology -- 3 Grammars -- 4 Outline -- 2. Survey of Evolutionary Automatic Programming -- 1 Introduction -- 2 Evolutionary Automatic Programming -- 3 Origin of the Species -- 4 Tree-based Systems -- 5 String based GP -- 6 Conclusions -- 3. Lessons from Molecular Biology -- 1 Introduction -- 2 Genetic Codes & Gene Expression Models -- 3 Neutral Theory of Evolution -- 4 Further Principles -- 5 Desirable Features -- 6 Conclusions -- 4. Grammatical Evolution -- 1 Introduction -- 2 Background -- 3 Grammatical Evolution -- 4 Discussion -- 5 Conclusions -- 5. Four Examples of Grammatical Evolution -- 1 Introduction -- 2 Symbolic Regression -- 3 Symbolic Integration -- 4 Santa Fe Ant Trail -- 5 Caching Algorithms -- 6 Conclusions -- 6. Analysis of Grammatical Evolution -- 1 Introduction -- 2 Wrapping Operator -- 3 Degenerate Genetic Code -- 4 Removal of Wrapping and Degeneracy -- 5 Mutation Rates -- 6 Conclusions -- 7. Crossover in Grammatical Evolution -- 1 Introduction -- 2 Homologous Crossover -- 3 Headless Chicken -- 4 Conclusions -- 8. Extensions & Applications -- 1 Translation -- 2 Alternative Search Strategies -- 3 Grammar Defined Introns -- 4 GAUGE -- 5 Chorus -- 6 Financial Prediction -- 7 Adaptive Logic Programming -- 8 Sensible Initialisation -- 9 Genetic Programming -- 10 Conclusions -- 9. Conclusions & Future Work -- 1 Summary -- 2 Future Work.
Síntesis:
Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language provides the first comprehensive introduction to Grammatical Evolution, a novel approach to Genetic Programming that adopts principles from molecular biology in a simple and useful manner, coupled with the use of grammars to specify legal structures in a search. Grammatical Evolution's rich modularity gives a unique flexibility, making it possible to use alternative search strategies - whether evolutionary, deterministic or some other approach - and to even radically change its behavior by merely changing the grammar supplied. This approach to Genetic Programming represents a powerful new weapon in the Machine Learning toolkit that can be applied to a diverse set of problem domains.
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Acceso electrónico:
Full Text Available From Springer Nature Computer Science Archive Packages
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