Genetic Programming
14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011, Proceedings
Published on 27. April 2011
XIII, 348 pages
978-3-642-20407-4 (ISBN)
Description
This book constitutes the refereed proceedings of the 14th European Conference on Genetic Programming, EuroGP 2011, held in Torino, Italy, in April 2011 co-located with the Evo* 2011 events.
This 20 revised full papers presented together with 9 poster papers were carefully reviewed and selected from 59 submissions. The wide range of topics in this volume reflect the current state of research in the field, including representations, theory, novel operators and techniques, self organization, and applications.
This 20 revised full papers presented together with 9 poster papers were carefully reviewed and selected from 59 submissions. The wide range of topics in this volume reflect the current state of research in the field, including representations, theory, novel operators and techniques, self organization, and applications.
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Sara Silva | James A. Foster | Miguel Nicolau
Genetic Programming
14th European Conference, EuroGP 2011, Torino, Italy, April 27-29, 2011, Proceedings
Book
04/2011
Springer
€53.49
Shipment within 7-9 days
Content
- Title
- Preface
- Organization
- Table of Contents
- Oral Presentations
- A Sniffer Technique for an Efficient Deduction of Model Dynamical Equations Using Genetic Programming
- Introduction
- Sniffer Enhanced GP Search Technique
- Deduction of Model Differential Equations
- Deduction of Chemical Reaction Model Equations
- Deduction of a Special Function ODE
- Deduction of KdV Equation
- Conclusion and Outlook
- References
- Robustness, Evolvability, and Accessibility in Linear Genetic Programming
- Introduction
- Methods
- Linear Genetic Programming
- Genotype and Phenotype Networks
- Robustness, Evolvability, and Accessibility
- Results
- Statistical Characteristics of Genotype and Phenotype Space
- Random Walks through Genotype and Phenotype Space
- Discussion
- References
- A Quantitative Study of Learning and Generalization in Genetic Programming
- Introduction
- Previous and Related Work
- The Proposed Measures
- Test Problems
- Experimental Study
- Conclusions and Future Work
- References
- GP-Based Electricity Price Forecasting
- Introduction
- Our Approach
- Overview
- GP Approach
- Hybrid Approach
- Experimental Evaluation
- Dataset and Baseline
- Settings
- Results
- Concluding Remarks
- References
- Novel Loop Structures and the Evolution of Mathematical Algorithms
- Introduction
- Related Work
- LoopRepresentations
- Test Problems
- Experiments
- Experimental Protocol
- Results in Terms of Fitness
- Results in Terms of Loop Effectiveness
- The Evolution of the Loop Utilization
- Conclusions
- References
- Maximum Margin Decision Surfaces for Increased Generalisation in Evolutionary Decision Tree Learning
- Introduction
- Model Generalisation in Genetically Programmed DTs
- Geometry of a Linear Discriminant Function
- Evolutionary Optimisation of a Maximum Margin Decision Surface via an ES(1+1)
- Methods
- Experimental Approach
- Grammar-Based Genetic Programming
- Results
- Conclusion
- References
- Evolving Cell Array Configurations Using CGP
- Introduction
- Motivation and Related Work
- Description of Cell Array
- CGP Overview
- Adaptation of CGP for Cell Array Evolution
- Basic Operation Changes
- Additional Genetic Operator, Node-Copy
- Applying Selection Pressure for Parsimony
- Performance Experiments
- Results and Discussion
- Comparison to Similar Techniques
- Node-Copy
- Size-Sort
- Conclusions and Further Work
- References
- Designing Pheromone Update Strategies with Strongly Typed Genetic Programming
- Introduction
- ACO Algorithms
- Evolving Pheromone Trail Update Methods
- Strongly Typed Genetic Programming Engine
- Related Work
- Experiments and Analysis
- Evolution of the Update Strategies
- Validation and Comparison with Max-Min Ant System
- Conclusions
- References
- Random Lines: A Novel Population Set-Based Evolutionary Global Optimization Algorithm
- Introduction
- Formulation
- Performance Evaluation
- Conclusion
- References
- A Peer-to-Peer Approach to Genetic Programming
- Introduction
- Description of the Model
- Neighbor Selection Policy: Newscast
- Experimental Setup and Results
- Conclusions and Future Works
- References
- Performance Models for Evolutionary Program Induction Algorithms Based on Problem Difficulty Indicators
- Introduction
- Related Work
- Algorithm Selection and Portfolios
- Models of Performance for Evolutionary Algorithms
- Modelling EPA's Performance
- Can NSC and FDC Be Used in Performance Models?
- Difficulty Indicators for Boolean Functions
- Difficulty Indicators for Continuous Functions
- Difficulty-Indicators Models
- Model Identification
- Test Problems and Systems
- Results
- Eliciting Knowledge from Our Models
- Conclusions
- References
- Examining Mutation Landscapes in Grammar Based Genetic Programming
- Introduction
- Landscapes
- Grammatical Evolution
- Grammatical Evolution by Example
- Tree-Adjunct Grammatical Evolution
- Tree-Adjunct Grammatical Evolution by Example
- Experiments and Results
- Experimental Setup
- Problems
- Visualisations
- Discussion
- Conclusions
- References
- ReNCoDe: A Regulatory Network Computational Device
- Introduction
- Banzhaf's ARN
- Architecture
- Problems and Experimental Setup
- Results and Analysis
- Conclusions and Future Work
- References
- Statistical Distribution of Generation-to-Success in GP: Application to Model Accumulated Success Probability
- Introduction
- Initial Definitions
- A General Model of Accumulated Success Probability
- Empirical Model of Generation-to-Success
- A Specific Model of Accumulated Success Probability
- Experimental Validation of the Model of Accumulated Success Probability
- Discussion
- Related Work
- Conclusions and Future Work
- References
- Learnable Embeddings of Program Spaces
- Introduction
- Programs, Semantics, and Locality of Semantic Mapping
- Example 1: Semantic Locality of Symbolic Regression Program Spaces
- Embedding Programs in Prespaces
- Example 2: Learning Prespaces for Symbolic Regression
- Learnable Embeddings and Prespaces as Tools for Search
- Discussion
- References
- Parallel Linear Genetic Programming
- Introduction
- Objectives
- Organization
- Background
- LGP
- Classification
- Classification Using LGP
- Parallel LGP
- Motivation
- Program Structure
- Crossover for PLGP
- Experimental Setup
- Data Sets
- Parameter Configurations
- Results
- Discussion
- Conclusions and Future Work
- References
- How Far Is It from Here to There? A Distance That Is Coherent with GP Operators
- Introduction
- Definitions
- Discussion of Definitions w.r.t. Previous Work
- Methods
- Representation
- Calculation of 1STP for Subtree Mutation
- Calculation of Graph-Based Distances
- Creating and Sampling from Sub-graphs
- Calculation of Syntactic Distances
- Results: Relationships among Distance Functions
- Distribution of DMSTP
- Learning DMSTP as a Function of Syntactic Distances
- Relationships among Graph-Based Distances
- Conclusions
- References
- Evolution of a Brain-Computer Interface Mouse via Genetic Programming
- Introduction
- BCI Mouse
- GP System and Parameter Settings
- Experimental Results
- Conclusions
- References
- Operator Self-adaptation in Genetic Programming
- Introduction
- Background
- Operator Parameter Adaptation
- Tree Adjoining Grammars and Genetic Programming
- Methods
- Problems
- Defining the LTAG3P System
- Adaptive Mechanisms
- Evolutionary Parameters
- Results
- Overall Performance
- Operator Application Rates
- Discussion
- Conclusions
- Summary
- Limitations
- Further Work
- References
- Evolving Fitness Functions for Mating Selection
- Introduction
- State of the Art
- Other Approaches
- Evolving the Fitness Function
- Circle Packing in Squares
- Mating Selection
- Mating Selection in the Circle Packing in Squares Problem
- Experimental Setup
- Experimental Results
- Conclusions
- References
- Posters
- Experiments on Islands
- Introduction
- LGP System Used
- The Test Cases
- Intertwined Spirals
- Regression
- 8-Bit Parity
- Topology Experiments
- Test Results
- Delaying Migrations
- Conclusion
- References
- A New Approach to Solving 0-1 Multiconstraint Knapsack Problems Using Attribute Grammar with Lookahead
- Introduction
- AGs
- Multiconstraint Knapsack Problem
- Previous Work
- Grammatical Evolution Approaches for the MKP
- AG with Lookahead for the MKP
- Experiments
- Results
- Conclusions and Future Work
- References
- An Empirical Study of Functional Complexity as an Indicator of Overfitting in Genetic Programming
- Introduction
- Overfitting
- Program Complexity
- Functional Complexity
- Experiments
- Summary and Concluding Remarks
- References
- Estimating Classifier Performance with Genetic Programming
- Introduction
- Related Work
- Problem Statement
- GP Approach
- Search Space
- Fitness Function
- Experiments
- Experimental Setup and Data-Sets
- Results and Comparisons
- Summary and Concluding Remarks
- References
- Investigation of the Performance of Different Mapping Orders for GE on the Max Problem
- Introduction
- Grammatical Evolution Essentials
- Genotype-Phenotype Maps - GE, GE
- Experimental Setup
- The Max Problem
- Mappers
- Results
- Discussion
- Conclusions and Future Work
- References
- A Self-scaling Instruction Generator Using Cartesian Genetic Programming
- Introduction
- CGP Decode for Instruction Generation
- Dual-Layer CGP Architecture
- Special Function Node
- Extended Parametric CGP
- Experiments
- Settings
- Results
- Conclusions and Future Work
- References
- Exploring Grammatical Modification with Modules in Grammatical Evolution
- Introduction
- Previous Work
- Experimental Setup
- Results and Discussion
- Grammar Modification
- Grammar Enhancement and Fitness
- Conclusion and Future Work
- References
- Multi-objective Genetic Programming for Visual Analytics
- Introduction
- The Multi-objective Genetic Programming Projection Pursuit (MOG3P) Algorithm
- Feature Extraction Criteria
- Standard Methods
- MOG3P Multi-objective Criteria
- Experiments
- Conclusions
- References
- A Continuous Approach to Genetic Programming
- Introduction
- Differential Evolution
- Linear Differential Evolutionary Programming
- Representation
- Implementation
- Symbolic Regression and Artifical Ant Experiments
- Symbolic Regression Problems
- Santa Fe Ant Trail
- Evolving a Stack with LDEP
- Architecture and Fitness Function
- Conclusion and Future Works
- References
- Author Index
