Genetic Programming comprises of proceedings of the 12th European Conference on Genetic Programming, EuroGP 2010. Topics include novel models, performance enhancements, extensions of genetic programming, and various applications.

Genetic Programming comprises of proceedings of the 12th European Conference on Genetic Programming, EuroGP 2010. Topics include novel models, performance enhancements, extensions of genetic programming, and various applications.

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.