Genetic Programming 23rd European Conference, EuroGP 2020, Held as Part of EvoStar 2020, Seville, Spain, April 15-17, 2020, Proceedings

• Main Authors: EuroGP, Hu, Ting, Lourenço, Nuno, Medvet, Eric, Divina, Federico, EVOSTAR
• Format: eBook Book
• Language: English
• Published: Netherlands Springer Nature 2020; Springer; Springer International Publishing AG
• Edition: 1
• Series: Lecture Notes in Computer Science
• Subjects: Data processing Computer science; Genetic programming (Computer science) > Congresses
• ISBN: 9783030440947; 303044094X; 9783030440930; 3030440931

This book constitutes the refereed proceedings of the 23rd European Conference on Genetic Programming, EuroGP 2020, held as part of Evo*2020, in Seville, Spain, in April 2020, co-located with the Evo*2020 events EvoCOP, EvoMUSART and EvoApplications.The 12 full papers and 6 short papers presented in...

Table of Contents:

• 4.3 Comparison Design -- 5 Results and Discussions -- 5.1 Performance of Evolved Rules -- 5.2 The Probability Difference -- 5.3 The Occurrences of Features -- 5.4 Training Time -- 6 Conclusions and Future Work -- References -- Classification of Autism Genes Using Network Science and Linear Genetic Programming -- 1 Introduction -- 2 Methods -- 2.1 Data Collection -- 2.2 Human Molecular Interaction Network -- 2.3 Linear Genetic Programming Algorithm -- 2.4 Implementation Settings -- 3 Results -- 3.1 Properties of the HMIN -- 3.2 Best Classification Models -- 3.3 Assessment of Feature Importance -- 3.4 Evaluation of Autism-Gene Prioritization -- 3.5 Independent Validation of Autism-Gene Prediction -- 4 Discussion -- References -- Author Index
• Investigating the Use of Geometric Semantic Operators in Vectorial Genetic Programming -- 1 Introduction -- 2 Panel Datasets in GP: Literature Review -- 3 Problem Description and the Datasets -- 3.1 Mosquito Abundance (P_Mosq) -- 3.2 Ventilation Flow (P_Physio) -- 4 Methodology -- 4.1 Vectorial Genetic Programming -- 4.2 Geometric Semantic Operators -- 5 Experiments -- 5.1 Experimental Settings -- 5.2 Experimental Results -- 6 Conclusions -- References -- Comparing Genetic Programming Approaches for Non-functional Genetic Improvement -- 1 Introduction -- 2 Genetic Improvement (GI) -- 2.1 Software Representations -- 2.2 Fitness Assessment -- 3 Genetic Programming (GP) -- 4 Experimental Setup -- 4.1 MiniSAT -- 4.2 Experimental Protocol -- 4.3 Search Processes -- 4.4 Filtering -- 5 Results and Discussion -- 5.1 Overall Training Results -- 5.2 Comparison of Approaches -- 5.3 Comparative Analysis -- 5.4 Research Questions -- 6 Conclusions -- References -- Automatically Evolving Lookup Tables for Function Approximation -- 1 Introduction -- 2 Background -- 2.1 Covariance Matrix Adaption - Evolution Strategy (CMA-ES) -- 2.2 Evolving Better Software Parameters -- 2.3 Investigating Evolving Better Software Parameters -- 3 Methods -- 3.1 CMA-ES Settings -- 3.2 Test Setup and Measurements -- 3.3 Fitness Function Design -- 4 Results -- 4.1 Run-Time Performance -- 4.2 Limitations -- 5 Conclusions and Outlook -- References -- Optimising Optimisers with Push GP -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Push and Push GP -- 3.2 Evolving Population-Based Optimisers -- 3.3 Evaluation -- 4 Results -- 5 Conclusions -- References -- An Evolutionary View on Reversible Shift-Invariant Transformations -- 1 Introduction -- 2 Background -- 2.1 Shift-Invariant Transformations and Cellular Automata -- 2.2 Reversible CA -- 2.3 Marker CA -- 3 Optimizing Landscapes
• 3.1 Genotype Representation for Marker CA -- 3.2 Fitness Functions -- 4 Related Work -- 5 Experiments -- 5.1 Research Questions and Experimental Setting -- 5.2 Single-Objective Approach -- 5.3 Multi-objective Approach -- 5.4 Lexicographic Optimization -- 6 Conclusions and Future Work -- References -- Benchmarking Manifold Learning Methods on a Large Collection of Datasets -- 1 Introduction -- 2 Methods -- 2.1 Manifold Learning Methods -- 2.2 ManiGP - A New Manifold Learning Method Based on Genetic Programming -- 2.3 Datasets -- 2.4 Methodology of Comparison -- 3 Results -- 4 Conclusions -- References -- Ensemble Genetic Programming -- 1 Introduction -- 2 Related Work -- 3 Ensemble GP -- 3.1 M3GP -- 3.2 eGP Population Structure -- 3.3 eGP Fitness Functions -- 3.4 eGP Genetic Operators -- 4 Experimental Setup -- 4.1 Methods -- 4.2 Parameters -- 4.3 Datasets -- 5 Results -- 6 Discussion -- 7 Conclusions and Future Work -- References -- SGP-DT: Semantic Genetic Programming Based on Dynamic Targets -- 1 Introduction -- 2 Methodology -- 3 Related Work -- 4 Evaluation -- 4.1 Methods -- 4.2 Evaluation Setup -- 4.3 Results and Discussion -- 5 Conclusion -- References -- Effect of Parent Selection Methods on Modularity -- 1 Introduction -- 2 Related Work -- 3 Parent Selection Algorithms -- 3.1 Lexicase Selection -- 3.2 Tournament Selection -- 3.3 Fitness-Proportionate Selection -- 4 Push and the Evolution of Modularity -- 5 Reuse Metric -- 6 Experimental Set-Up -- 7 Results -- 8 Discussion -- 9 Conclusions and Future Work -- References -- Time Control or Size Control? Reducing Complexity and Improving Accuracy of Genetic Programming Models -- 1 Introduction -- 2 Background -- 2.1 Complexity in Genetic Programming -- 2.2 Evaluating Time Is More Than Measuring Size -- 2.3 Stabilising Evaluation Time Measurements -- 3 Experiments -- 3.1 Bloat Control Techniques
• 3.2 Test Problems -- 3.3 Configuration and Parameters -- 3.4 Initialising the Population -- 4 Results -- 4.1 Discussion -- 5 Conclusions and Future Work -- References -- Challenges of Program Synthesis with Grammatical Evolution -- 1 Introduction -- 2 Related Work -- 3 Methodology -- 3.1 Software Metrics -- 3.2 Program Synthesis Problems -- 3.3 GE Grammar and Fitness Function -- 4 Experiments and Discussion -- 4.1 Robustness of Reference Implementations: Part I -- 4.2 Robustness of Reference Implementations: Part II -- 4.3 Search Behavior of GE -- 4.4 Search for the Needle in a Haystack -- 5 Conclusions -- References -- Detection of Frailty Using Genetic Programming -- 1 Introduction -- 2 Methods -- 2.1 Data Source -- 2.2 Data Transformation -- 2.3 Learning from Imbalanced Data -- 3 Experiments -- 3.1 GP Parameter Setup -- 4 Results -- 4.1 GP Prediction Performance -- 4.2 Performance of Other Non-GP Classifiers -- 4.3 Feature Selection Comparison of GP and Chi-Square -- 5 Discussions and Conclusions -- References -- Is k Nearest Neighbours Regression Better Than GP? -- 1 Introduction -- 2 Geometric Semantic Genetic Programming -- 3 Random Vector Based Mutation -- 4 Experimental Study -- 4.1 Test Problems and Experimental Settings -- 4.2 Experimental Results: RVMGP vs GSGP -- 4.3 Experimental Results: RVMGP vs KNN Regression vs RF Regression -- 5 Conclusions and Future Work -- References -- Guided Subtree Selection for Genetic Operators in Genetic Programming for Dynamic Flexible Job Shop Scheduling -- 1 Introduction -- 2 Background -- 2.1 Dynamic Flexible Job Shop Scheduling -- 2.2 Genetic Programming Hyper-heuristic for DFJSS -- 3 The Proposed GP with Subtree Selection -- 3.1 The Occurrences of Features -- 3.2 The Importance of Subtrees -- 3.3 Subtree Selection -- 3.4 Summary -- 4 Experiment Design -- 4.1 Simulation Model -- 4.2 Parameter Settings
• Intro -- Preface -- Organization -- Contents -- Hessian Complexity Measure for Genetic Programming-Based Imputation Predictor Selection in Symbolic Regression with Incomplete Data -- 1 Introduction -- 2 Background -- 2.1 Missing Value Imputation -- 2.2 Model Complexity in GP -- 2.3 GP for Feature Selection -- 2.4 Symbolic Regression with Incomplete Data -- 3 The Proposed Method -- 3.1 The Overall System -- 3.2 Standard GP-Based Predictor Selection -- 3.3 GP-Based Predictor Selection with Feature Selection Pressure -- 3.4 The Proposed Method: GP-Based Predictor Selection with Model Complexity Pressure -- 4 Experiment Setup -- 5 Results and Discussions -- 5.1 Imputation Performance -- 5.2 Symbolic Regression Performance -- 5.3 The Number of Selected Predictors -- 6 Conclusions and Future Work -- References -- Seeding Grammars in Grammatical Evolution to Improve Search Based Software Testing -- 1 Introduction -- 2 Background and Related Work -- 2.1 Evolutionary Testing -- 2.2 SBST Techniques Benefitting from Seeding -- 3 Ariadne: GE-Based Test Data Generation -- 3.1 Grammatical Evolution -- 3.2 Grammar -- 4 Improved Grammar -- 4.1 Philosophy Behind the Proposed Changes -- 5 Experimental Results and Discussion -- 5.1 Experimental Setup -- 5.2 Detailed Analysis of Experiments -- 6 Conclusion and Future Work -- References -- Incremental Evolution and Development of Deep Artificial Neural Networks -- 1 Introduction -- 2 Related Work -- 3 Fast-DENSER -- 4 Incremental Development of Deep Neural Networks -- 5 Experimentation -- 5.1 Datasets -- 5.2 Experimental Setup -- 5.3 Experimental Results: Incremental Development -- 5.4 Experimental Results: Topology Analysis -- 5.5 Experimental Results: Generalisation of the Models -- 5.6 Discussion -- 6 Conclusions and Future Work -- References