Genetic Programming

Name: Genetic Programming | 19th European Conference, EuroGP 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings
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19th European Conference, EuroGP 2016, Porto, Portugal, March 30 - April 1, 2016, Proceedings

Malcolm I. Heywood James McDermott Mauro Castelli Ernesto Costa Kevin Sim(Editor)

Springer (Publisher)

Published on 23. March 2016

XII, 311 pages

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978-3-319-30668-1 (ISBN)

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Content

Intro
Preface
Organization
Contents
Full Presentations
One-Class Classification for Anomaly Detection with Kernel Density Estimation and Genetic Programming
1 Introduction
2 Related Work
3 Preliminaries
3.1 Genetic Programming
3.2 Kernel Density Estimation
4 Proposed Approach
4.1 Description of Method
4.2 Generating Artificial Data
5 Experiments
5.1 Datasets
5.2 Experimental Settings
6 Results and Discussion
7 Conclusion and Further Work
References
Evolutionary Approximation of Edge Detection Circuits
1 Introduction
2 Relevant Work
2.1 Edge Detectors
2.2 Evolutionary Computing in Edge Detector Design
2.3 Approximate Computing in Image Processing
2.4 Evolutionary Circuit Design
3 Adopting CGP for Circuit Approximation
3.1 Cartesian Genetic Programming
3.2 Resources-Oriented Approximation
4 Experimental Results
4.1 Evolutionary Approximation of Adders
4.2 Approximation of Sobel Edge Detector
4.3 Approximation of Evolved Edge Operator
5 Conclusions
References
On the Impact of Class Imbalance in GP Streaming Classification with Label Budgets
1 Introduction
2 Related Work
3 Methodology
3.1 Anytime Operation
3.2 Archiving Policy
3.3 Sampling Policy
4 Experimental Methodology
4.1 Datasets
4.2 Class-Wise Detection Rate
4.3 Parameters
5 Results
5.1 Single Generation Performance
5.2 Multi-generation Performance
5.3 Overall Detection Rates
6 Conclusion
References
Genetic Programming for Region Detection, Feature Extraction, Feature Construction and Classification in Image Data
1 Introduction
2 Background
2.1 Related Work
3 The Proposed Method
3.1 GP Program Representation
3.2 Outline of the HoG Function
3.3 The Fitness Function
4 Experiment Design
4.1 Datasets
4.2 Training and Test Sets
4.3 Baseline Methods
4.4 Generating SURF Keypoints
4.5 Evolutionary Parameters
5 Results and Discussion
5.1 Compared to the 2TGP Approach
5.2 Compared to the Baselines
6 Further Analysis
6.1 Example Program 1
6.2 Example Program 2
6.3 Example Program 3
7 Conclusions
References
Surrogate Fitness via Factorization of Interaction Matrix
1 Introduction
2 Background
3 Factorization of Interaction Matrix
4 The SFIMX Algorithm
5 Related Work
6 Experimental Verification
7 Conclusions and Future Work
References
Scheduling in Heterogeneous Networks Using Grammar-Based Genetic Programming
1 Introduction
2 Problem Definition
3 Previous Work
4 Simulation Environment
4.1 Generating Inputs
4.2 Calculating Fitness
5 Experiments
6 Results and Discussion
6.1 Terminal Utilisation
6.2 Subframe Utilisation
6.3 Benchmarking
7 Future Work and Conclusions
References
On the Analysis of Simple Genetic Programming for Evolving Boolean Functions
1 Introduction
2 Preliminaries
3 Analysis for Complete Training Sets
3.1 Analysis for ANDn with Complete Training Sets
3.2 Analysis for XORn with Complete Training Sets
4 Analysis for Incomplete Training Sets
4.1 Analysis for ANDn with Incomplete Training Set
4.2 Analysis for XORn with Incomplete Training Set
5 Conclusions
References
Genetic Programming Based Hyper-heuristics for Dynamic Job Shop Scheduling: Cooperative Coevolutionary Approaches
1 Introduction
2 Background
2.1 Dynamic Job Shop Scheduling
2.2 Genetic Programming Based Hyper-heuristics for Dynamic JSS
2.3 Cooperative Coevolution in Genetic Programming
3 Coevolutionary GP Approaches to JSS: MLGP-JSS and EGP-JSS
3.1 MLGP-JSS Process Overview
3.2 Selection
3.3 Evaluation Procedure
3.4 EGP-JSS Process Overview
3.5 GP Representation, Terminals and Function Sets
4 Experimental Design
4.1 Dataset
4.2 GP-HH Benchmark Methods for Comparison
4.3 Parameter Settings
5 Results
5.1 MLGP-JSS vs GP-JSS
5.2 MLGP-JSS vs EGP-JSS
6 Conclusions
References
A Genetic Programming Approach for the Traffic Signal Control Problem with Epigenetic Modifications
1 Introduction
2 Epigenetics
3 Traffic Signal Control
4 The Traffic Simulator
4.1 Traffic Network
4.2 Vehicle Insertion
5 Representation
6 The Epigenetic Mechanism
7 Experiments
8 Results and Discussion
9 Conclusions and Future Work
A Traffic Parameteres
References
A Genetic Programming-Based Imputation Method for Classification with Missing Data
1 Introduction
1.1 Research Goals
1.2 Organisation
2 Related Work
2.1 Classification with Missing Data
2.2 Imputation Methods
2.3 Genetic Programming-Based Symbolic Regression
3 Genetic Programming-Based Imputation for Classification with Missing Data
4 Experiment Design
4.1 Method
4.2 Datasets
4.3 Benchmark Imputation Methods for Comparison
4.4 Classification Algorithms
4.5 GP Settings
5 Results and Analysis
5.1 Classification Accuracy
5.2 Computation Time
6 Conclusion and Future Work
References
Plastic Fitness Predictors Coevolved with Cartesian Programs
1 Introduction
2 Fitness Prediction in CGP
2.1 Fitness Predictor
2.2 Coevolution of Cartesian Programs and Fitness Predictors
3 Proposed Method
3.1 Plastic Directly Encoded Predictor
3.2 Predictor Size Adaptation
4 Results
4.1 Benchmark Problems
4.2 Experimental Setup
4.3 Ability to Adapt the Number of Fitness Cases
4.4 Predictor Behaviour
4.5 Comparison of the Predictor Size
4.6 Comparisons of Various Approaches to Fitness Prediction in CGP
5 Conclusions
References
Short Presentations
Search-Based SQL Injection Attacks Testing Using Genetic Programming
1 Introduction
2 Related Work
3 SQL Injection Attacks (SQLIAs)
3.1 Tautologies
3.2 Union Query
3.3 Piggyback Queries
3.4 Malformed Queries
3.5 Inference Queries
4 Design of the GP Grammar
4.1 Terminal Sets
4.2 Functions Sets
4.3 Fitness Function
4.4 Parameters
4.5 Termination and Solution Designation
5 System Design and Implementation
5.1 Representation of Individuals
5.2 Evaluation and Fitness
6 Results and Analysis
7 Conclusion and Future Work
References
Grammar Design for Derivation Tree Based Genetic Programming Systems
1 Introduction
2 Grammars in Evolutionary Systems
2.1 Grammar Guided Genetic Programming
2.2 Grammar Design
2.3 Structure in Grammars
3 Experimental Setup
3.1 Sorting Network
3.2 Experimental Grammar Design
3.3 Experiments
4 Results
4.1 Experiment 1
4.2 Experiment 2
5 Conclusion and Future Work
References
Modelling Evolvability in Genetic Programming
1 Introduction
2 Related Work
3 Approach
4 Experimental Design and Results
4.1 Sampling Accuracy
4.2 Selection of Evolvability
4.3 Modelling of Evolvability
References
Towards Automated Strategies in Satisfiability Modulo Theory
1 Introduction
2 Strategies and SMT Logics
2.1 Strategies
2.2 SMT Logics
3 Evolutionary Algorithm
4 Experimental Setup
5 Results
5.1 LIA Benchmarks Set
5.2 LRA Benchmarks Set
5.3 QF_LIA Benchmarks Set
5.4 QF_LRA Benchmarks Set
6 Related Work
7 Conclusion and Future Work
References
Geometric Semantic Genetic Programming Is Overkill
1 Introduction
2 Problem Statement and Solution in Geometric Semantic Genetic Programming
3 Function Learning Using Linear Combination
3.1 Symbolic Regression
3.2 Boolean Function Synthesis
3.3 Classifier Induction
4 Experiment
4.1 Setup
4.2 Results
5 Discussion
6 Conclusions
References
Semantic Geometric Initialization
1 Introduction
2 Background
3 The Problem
4 Semantic Geometric Initialization
5 Related Work
6 Experimental Verification
7 Discussion
8 Conclusions
References
Patterns for Constructing Mutation Operators: Limiting the Search Space in a Software Engineering Application
1 Introduction
2 Model Transformations by Example and Related Work
3 GP Approach to Evolve Model-to-Model Transformations
4 Implementation and Example
5 Results
6 Conclusion
References
Iterative Cartesian Genetic Programming: Creating General Algorithms for Solving Travelling Salesman Problems
1 Introduction
2 Optimisation of Algorithms
3 Iterative Cartesian Genetic Programming
4 Discovery of Iterative TSP Solvers
4.1 The Travelling Salesman Problem
4.2 Automatic Design of Hybrid Metaheuristics
5 Experimental Results
6 Conclusion
References
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