Genetic Programming

<p>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 Problem Class.- 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.</p>