Genetic Programming - Taschenbuch

EAN (ISBN-13): 9783540716020
ISBN (ISBN-10): 3540716025
Gebundene Ausgabe
Taschenbuch
Erscheinungsjahr: 2007
Herausgeber: Springer Berlin Heidelberg
382 Seiten
Gewicht: 0,608 kg
Sprache: eng/Englisch

Buch in der Datenbank seit 2007-07-10T20:43:38+02:00 (Berlin)
Detailseite zuletzt geändert am 2023-10-15T10:37:37+02:00 (Berlin)
ISBN/EAN: 9783540716020

ISBN - alternative Schreibweisen:
3-540-71602-5, 978-3-540-71602-0
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: michael ebner, vannes, anna leo, aniko, leo van, leonardo, michael neill
Titel des Buches: genetic programming, 10th, valencia, april

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Daten vom Verlag:

Autor/in: Marc Ebner; Michael O'Neill; Anikó Ekárt; Leonardo Vanneschi; Anna Isabel Esparcia-Alcázar
Titel: Lecture Notes in Computer Science; Theoretical Computer Science and General Issues; Genetic Programming - 10th European Conference, EuroGP 2007, Valencia, Spain, April 11-13, 2007, Proceedings
Verlag: Springer; Springer Berlin
382 Seiten
Erscheinungsjahr: 2007-04-02
Berlin; Heidelberg; DE
Sprache: Englisch
53,49 € (DE)
54,99 € (AT)
59,00 CHF (CH)
Available
XI, 382 p.

BC; Hardcover, Softcover / Informatik, EDV/Informatik; Software Engineering; Verstehen; Informatik; algorithm; algorithms; data mining; genetic programming; learning; optimization; programming; real-time; algorithm analysis and problem complexity; Software Engineering; Programming Techniques; Theory of Computation; Algorithms; Automated Pattern Recognition; Artificial Intelligence; Computerprogrammierung und Softwareentwicklung; Theoretische Informatik; Algorithmen und Datenstrukturen; Mustererkennung; Künstliche Intelligenz; EA

Plenary Talks.- A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms.- An Empirical Boosting Scheme for ROC-Based Genetic Programming Classifiers.- Confidence Intervals for Computational Effort Comparisons.- Crossover Bias in Genetic Programming.- Density Estimation with Genetic Programming for Inverse Problem Solving.- Empirical Analysis of GP Tree-Fragments.- Empirical Comparison of Evolutionary Representations of the Inverse Problem for Iterated Function Systems.- Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess.- Fast Genetic Programming on GPUs.- FIFTHTM: A Stack Based GP Language for Vector Processing.- Genetic Programming with Fitness Based on Model Checking.- Geometric Particle Swarm Optimisation.- GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions.- Layered Learning in Boolean GP Problems.- Mining Distributed Evolving Data Streams Using Fractal GP Ensembles.- Multi-objective Genetic Programming for Improving the Performance of TCP.- On Population Size and Neutrality: Facilitating the Evolution of Evolvability.- On the Limiting Distribution of Program Sizes in Tree-Based Genetic Programming.- Predicting Prime Numbers Using Cartesian Genetic Programming.- Real-Time, Non-intrusive Evaluation of VoIP.- Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach.- Posters.- A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds.- Analysing the Regularity of Genomes Using Compression and Expression Simplification.- Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming.- Code Regulation in Open Ended Evolution.- Data Mining of Genetic Programming Run Logs.- Evolving a Statistics Class Using Object Oriented Evolutionary Programming.- Evolving Modular Recursive Sorting Algorithms.- Fitness Landscape Analysis and Image Filter Evolution Using Functional-Level CGP.- Genetic Programming Heuristics for Multiple Machine Scheduling.- Group-Foraging with Particle Swarms and Genetic Programming.- Multiple Interactive Outputs in a Single Tree: An Empirical Investigation.- Parsimony Doesn’t Mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data.- The Holland Broadcast Language and the Modeling of Biochemical Networks.- The Induction of Finite Transducers Using Genetic Programming.