Overview

In its lucky 12+1 edition, during April 7-9, 2010, the European Conference on Genetic Programming (EuroGP) travelled to its most easterly location so far, theEuropeanCityof Culture2010,Istanbul,Turkey.EuroGPisthe onlyconf- enceworldwideexclusivelydevotedtogeneticprogrammingandtheevolutionary generation of computer programs. For over a decade, genetic programming (GP) has been considered the new form of evolutionary computation. With nearly 7,000 articles in the online GP bibliography maintained by William B. Langdon, we can say that it is now a mature ?eld. EuroGP has contributed to the success of the ?eld substantially, by being a unique forum for expressing new ideas, meeting, and starting up collaborations. The wide rangeoftopics in this volume re?ectthecurrentstateof researchin the ?eld, including representations, theory, operators and analysis, novel m- els, performance enhancements, extensions of genetic programming,and various applications. The volume contains contributions in the following areas: - Understanding GP behavior andGP analysis include articles on cro- over operators and a new way of analyzing results. -GPperformance presents work on performance enhancements through phenotypic diversity, simpli?cation, ?tness and parallelism. - Novel models and their application present innovative approaches with arti?cial biochemical networks, genetic regulatory networks and geometric di?erential evolution. - Grammatical evolution introduces advances in crossover, mutation and phenotype-genotype maps in this relatively new area. - Machine learning and data mining include articles that present data miningormachinelearningsolutionsusingGPandalsocombinedatamining and machine learning with GP. - Applications rangefromsolvingdi?erentialequations,routingproblems to ?le type detection, object-oriented testing, agents. This year we received 48 submissions, of which 47 were sent to the reviewers.

Full Product Details

Author:   Anna Isabel Esparcia-Alcazar ,  Aniko Ekart ,  Sara Silva ,  Stephen Dignum
Publisher:   Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
Imprint:   Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Volume:   6021
Dimensions:   Width: 15.70cm , Height: 1.80cm , Length: 23.40cm
Weight:   0.539kg
ISBN:  

9783642121470

ISBN 10:   3642121470
Pages:   336
Publication Date:   25 March 2010
Audience:   Professional and scholarly ,  Professional & Vocational
Format:   Paperback
Publisher's Status:   Active
Availability:   Out of stock  
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Table of Contents

Oral Presentations.- Genetic Programming for Classification with Unbalanced Data.- An Analysis of the Behaviour of Mutation in Grammatical Evolution.- Positional Effect of Crossover and Mutation in Grammatical Evolution.- Sub-tree Swapping Crossover and Arity Histogram Distributions.- Novelty-Based Fitness: An Evaluation under the Santa Fe Trail.- An Analysis of Genotype-Phenotype Maps in Grammatical Evolution.- Handling Different Categories of Concept Drifts in Data Streams Using Distributed GP.- An Indirect Approach to the Three-Dimensional Multi-pipe Routing Problem.- Phenotypic Diversity in Initial Genetic Programming Populations.- A Relaxed Approach to Simplification in Genetic Programming.- Unsupervised Problem Decomposition Using Genetic Programming.- GP-Fileprints: File Types Detection Using Genetic Programming.- A Many Threaded CUDA Interpreter for Genetic Programming.- Controlling Complex Dynamics with Artificial Biochemical Networks.- Geometric Differential Evolution on the Space of Genetic Programs.- Improving the Generalisation Ability of Genetic Programming with Semantic Similarity based Crossover.- Evolving Genes to Balance a Pole.- Solution-Locked Averages and Solution-Time Binning in Genetic Programming.- Enabling Object Reuse on Genetic Programming-Based Approaches to Object-Oriented Evolutionary Testing.- Analytic Solutions to Differential Equations under Graph-Based Genetic Programming.- Learning a Lot from Only a Little: Genetic Programming for Panel Segmentation on Sparse Sensory Evaluation Data.- Posters.- Genetic Programming for Auction Based Scheduling.- Bandit-Based Genetic Programming.- Using Imaginary Ensembles to Select GP Classifiers.- Analysis of Building Blocks with Numerical Simplification in Genetic Programming.- Fast Evaluation of GP Trees on GPGPU by Optimizing Hardware Scheduling.- Ensemble Image Classification Method Based on Genetic Image Network.- Fine-Grained Timing Using Genetic Programming.

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