Genetic Programming
Description
The book constitutes the refereed proceedings of the 17th European Conference on Genetic Programming, Euro GP 2014, held in Grenada, Spain, in April 2014 co-located with the Evo*2014 events, Evo BIO, Evo COP, Evo MUSART and Evo Applications.
The 15 revised full papers presented together with 5 poster papers were carefully reviewed and selected form 40 submissions. The wide range of topics in this volume reflects the current state of research in the field. Thus, we see topics as diverse as search-based software engineering, image analysis, dynamical systems, evolutionary robotics and operational research to the foundations of search as characterized through semantic variation operators.
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Content
Higher Order Functions for Kernel Regression.- Flash: A GP-GPU Ensemble Learning System for Handling Large Datasets.- Learning Dynamical Systems Using Standard Symbolic Regression.- Semantic Crossover Based on the Partial Derivative Error.- A Multi-dimensional Genetic Programming Approach for Multi-class Classification Problems.- Generalisation Enhancement via Input Space Transformation: A GP Approach.- On Diversity, Teaming, and Hierarchical Policies: Observations from the Keepaway Soccer Task.- Genetically Improved CUDA C++ Software.- Measuring Mutation Operators' Exploration-Exploitation Behaviour and Long-Term Biases.- Exploring the Search Space of Hardware / Software Embedded Systems by Means of GP.- Enhancing Branch-and-Bound Algorithms for Order Acceptance and Scheduling with Genetic Programming.- Using Genetic Improvement and Code Transplants to Specialise a C++ Program to a ProblemClass.- ESAGP - A Semantic GP Framework Based on Alignment in the Error Space.- Building a Stage 1 Computer Aided Detector for Breast Cancer Using Genetic Programming.- NEAT, There's No Bloat.- The Best Things Don't Always Come in Small Packages: Constant Creation in Grammatical Evolution.- Asynchronous Evolution by Reference-Based Evaluation: Tertiary Parent Selection and Its Archive.- Behavioral Search Drivers for Genetic Programing.- Cartesian Genetic Programming: Why No Bloat.- On Evolution of Multi-category Pattern Classifiers Suitable for Embedded Systems.