Genetic Programming

Inhaltsverzeichnis

• Plenary Talks.
• A Grammatical Genetic Programming Approach to Modularity in Genetic Algorithms.
• An Empirical Boosting Scheme for ROC-Based Genetic Programming Classifiers.
• Confidence Intervals for Computational Effort Comparisons.
• Crossover Bias in Genetic Programming.
• Density Estimation with Genetic Programming for Inverse Problem Solving.
• Empirical Analysis of GP Tree-Fragments.
• Empirical Comparison of Evolutionary Representations of the Inverse Problem for Iterated Function Systems.
• Evolution of an Efficient Search Algorithm for the Mate-In-N Problem in Chess.
• Fast Genetic Programming on GPUs.
• FIFTHTM: A Stack Based GP Language for Vector Processing.
• Genetic Programming with Fitness Based on Model Checking.
• Geometric Particle Swarm Optimisation.
• GP Classifier Problem Decomposition Using First-Price and Second-Price Auctions.
• Layered Learning in Boolean GP Problems.
• Mining Distributed Evolving Data Streams Using Fractal GP Ensembles.
• Multi-objective Genetic Programming for Improving the Performance of TCP.
• On Population Size and Neutrality: Facilitating the Evolution of Evolvability.
• On the Limiting Distribution of Program Sizes in Tree-Based Genetic Programming.
• Predicting Prime Numbers Using Cartesian Genetic Programming.
• Real-Time, Non-intrusive Evaluation of VoIP.
• Training Binary GP Classifiers Efficiently: A Pareto-coevolutionary Approach.
• Posters.
• A Comprehensive View of Fitness Landscapes with Neutrality and Fitness Clouds.
• Analysing the Regularity of Genomes Using Compression and Expression Simplification.
• Changing the Genospace: Solving GA Problems with Cartesian Genetic Programming.
• Code Regulation in Open Ended Evolution.
• Data Mining of Genetic Programming Run Logs.
• Evolving a Statistics Class Using Object Oriented Evolutionary Programming.
• Evolving Modular Recursive Sorting Algorithms.
• Fitness Landscape Analysis and Image Filter Evolution Using Functional-Level CGP.
• Genetic Programming Heuristics for Multiple Machine Scheduling.
• Group-Foraging with Particle Swarms and Genetic Programming.
• Multiple Interactive Outputs in a Single Tree: An Empirical Investigation.
• Parsimony Doesn’t Mean Simplicity: Genetic Programming for Inductive Inference on Noisy Data.
• The Holland Broadcast Language and the Modeling of Biochemical Networks.
• The Induction of Finite Transducers Using Genetic Programming.