Genetic Programming

Anna Isabel Esparcia-Alcazar

ISBN 10: 3642121470 / ISBN 13: 9783642121470

Editore: Springer Berlin Heidelberg Mrz 2010, 2010

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This item is printed on demand - it takes 3-4 days longer - Neuware -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. 352 pp. Englisch.

Codice articolo 9783642121470

Dati bibliografici

• Titolo: Genetic Programming
• Editore: Springer Berlin Heidelberg Mrz 2010
• Data di pubblicazione: 2010
• Lingua: Inglese
• ISBN 10: 3642121470
• ISBN 13: 9783642121470
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 235 L x 155 A x 20 P (millimetri) 235 millimetri di larghezza per 155 millimetri di altezza per 20 millimetri di profondità
• Peso: 534 grammi

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Riassunto

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.

Contenuti

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.