Genetic Programming (eBook, PDF)

10th European Conference, EuroGP 2007, Valencia, Spain, April 11-13, 2007, Proceedings
Redaktion: Ebner, Marc; Esparcia-Alcázar, Anna Isabel; Vanneschi, Leonardo; Ekárt, Anikó; O'Neill, Michael

• Format: PDF

Produktbeschreibung

This book constitutes the refereed proceedings of the 10th European Conference on Genetic Programming, EuroGP 2007, held in Valencia, Spain in April 2007 colocated with EvoCOP 2007. The 21 revised plenary papers and 14 revised poster papers were carefully reviewed and selected from 71 submissions. The papers address fundamental and theoretical issues, along with a wide variety of papers dealing with different application areas.

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Produktdetails

• Verlag: Springer-Verlag GmbH
• Seitenzahl: 382
• Erscheinungstermin: 20. Juni 2007
• Englisch
• ISBN-13: 9783540716051
• Artikelnr.: 44128463

Autorenporträt

Michael O'Neill [BSc. (UCD), PhD (UL)] is a lecturer in the Department of Computer Science and Information Systems at the University of Limerick. He has over 70 publications on biologically inspired algorithms (BIAs). He coauthored the Springer title "Grammatical Evolution -- Evolutionary Automatic Programming in an Arbitrary Language", Genetic Programming Series, 2003, 160 pp., ISBN 1-4020-7444-1. He is one of the two original developers of the Grammatical Evolution algorithm, research that spawned an annual invited tutorial at the largest evolutionary computation conference and an international workshop, and is also on a number of relevant organising committees (e.g., GECCO 2005). Michael is a regular reviewer for the leading evolutionary computation (EC) journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal.

Inhaltsangabe

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.