Genetic Programming

Michael O'Neill [BSc. (UCD), PhD (UL)] is a lecturer in the Department of Computer Science and Information Systems at the University of Limerick. He has over 70 publications on biologically inspired algorithms (BIAs). He coauthored the Springer title "Grammatical Evolution -- Evolutionary Automatic Programming in an Arbitrary Language", Genetic Programming Series, 2003, 160 pp., ISBN 1-4020-7444-1. He is one of the two original developers of the Grammatical Evolution algorithm, research that spawned an annual invited tutorial at the largest evolutionary computation conference and an international workshop, and is also on a number of relevant organising committees (e.g., GECCO 2005). Michael is a regular reviewer for the leading evolutionary computation (EC) journals, namely IEEE Trans. on Evolutionary Computation, MIT Press's Evolutionary Computation, and Springer's Genetic Programming and Evolvable Hardware journal.

The 11th European Conference on Genetic Programming, EuroGP 2008, took place in Naples, Italy from 26 to 28 March in the University of Naples Congress Centre with spectacular views over the Gulf of Naples. This volume contains the papers for the 21 oral presentations and 10 posters that were presented during this time. A diverse array of topics were covered re?ecting the current state of research in the ?eld of Genetic Programming, including the latest work on representations, theory, operators and analysis, evolvable hardware, agents and numerous applications. A rigorous, double-blind peer review process was employed, with each s- mission reviewed by at least three members of the international Program C- mittee. In total 61 papers were submitted this year, making an acceptance rate of 34% for full papers, and an overall acceptance rate of 51% including posters. S- mission of papers and the reviewing process were greatly assisted by the use of the MyReview management software originally developed by Philippe Rigaux, Bertrand Chardon and other colleagues from the Universit´e Paris-Sud Orsay, France. We are especially grateful to Marc Schoenauer from INRIA, France for managing this system. Reviewers were asked to nominate keywords specifying their area of expertise, and these keywords were matched to those selected by the authors of the submitted papers with the assistance of the optimal assignment feature of the conference management software.

This proceedings volume examines the current state of research in the field of genetic programming, including the latest work on representations, theory, operators and analysis, evolvable hardware, agents and numerous applications.

Oral Presentations.- Training Time and Team Composition Robustness in Evolved Multi-agent Systems.- Winning Ant Wars: Evolving a Human-Competitive Game Strategy Using Fitnessless Selection.- In Silicon No One Can Hear You Scream: Evolving Fighting Creatures.- Real-Time, Non-intrusive Speech Quality Estimation: A Signal-Based Model.- Good News: Using News Feeds with Genetic Programming to Predict Stock Prices.- A Genetic Programming Approach to Deriving the Spectral Sensitivity of an Optical System.- A SIMD Interpreter for Genetic Programming on GPU Graphics Cards.- Partitioned Incremental Evolution of Hardware Using Genetic Programming.- Population Parallel GP on the G80 GPU.- Operator Equalisation and Bloat Free GP.- Practical Model of Genetic Programming's Performance on Rational Symbolic Regression Problems.- Semantic Building Blocks in Genetic Programming.- A Simple Powerful Constraint for Genetic Programming.- Crossover, Sampling, Bloat and the Harmful Effects of Size Limits.- The Performance of a Selection Architecture for Genetic Programming.- A Comparison of Cartesian Genetic Programming and Linear Genetic Programming.- Evolvability Via Modularity-Induced Mutational Focussing.- A Linear Estimation-of-Distribution GP System.- Feature Discovery in Reinforcement Learning Using Genetic Programming.- Hardware Accelerators for Cartesian Genetic Programming.- Genetic Programming and Class-Wise Orthogonal Transformation for Dimension Reduction in Classification Problems.- Posters.- Evolving Proactive Aggregation Protocols.- GP Classification under Imbalanced Data sets: Active Sub-sampling and AUC Approximation.- Exposing a Bias Toward Short-Length Numbers in Grammatical Evolution.- Cooperative Problem Decomposition in Pareto Competitive Classifier Models ofCoevolution.- Integrating Categorical Variables with Multiobjective Genetic Programming for Classifier Construction.- The Effects of Constant Neutrality on Performance and Problem Hardness in GP.- Applying Cost-Sensitive Multiobjective Genetic Programming to Feature Extraction for Spam E-mail Filtering.- PlasmidPL: A Plasmid-Inspired Language for Genetic Programming.- Using Genetic Programming for Turing Machine Induction.- Altering Search Rates of the Meta and Solution Grammars in the mGGA.