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Genetic Programming [[electronic resource] ] : 22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings / / edited by Lukas Sekanina, Ting Hu, Nuno Lourenço, Hendrik Richter, Pablo García-Sánchez
| Genetic Programming [[electronic resource] ] : 22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings / / edited by Lukas Sekanina, Ting Hu, Nuno Lourenço, Hendrik Richter, Pablo García-Sánchez |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XII, 295 p. 130 illus., 51 illus. in color.) |
| Disciplina | 006.3823 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Artificial intelligence Data mining Computer science—Mathematics Discrete mathematics Artificial intelligence—Data processing Pattern recognition systems Artificial Intelligence Data Mining and Knowledge Discovery Discrete Mathematics in Computer Science Data Science Automated Pattern Recognition |
| ISBN | 3-030-16670-8 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996466571403316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Genetic Programming : 22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings / / edited by Lukas Sekanina, Ting Hu, Nuno Lourenço, Hendrik Richter, Pablo García-Sánchez
| Genetic Programming : 22nd European Conference, EuroGP 2019, Held as Part of EvoStar 2019, Leipzig, Germany, April 24–26, 2019, Proceedings / / edited by Lukas Sekanina, Ting Hu, Nuno Lourenço, Hendrik Richter, Pablo García-Sánchez |
| Edizione | [1st ed. 2019.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 |
| Descrizione fisica | 1 online resource (XII, 295 p. 130 illus., 51 illus. in color.) |
| Disciplina | 006.3823 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Artificial intelligence Data mining Computer science—Mathematics Discrete mathematics Artificial intelligence—Data processing Pattern recognition systems Artificial Intelligence Data Mining and Knowledge Discovery Discrete Mathematics in Computer Science Data Science Automated Pattern Recognition |
| ISBN | 3-030-16670-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910337848203321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2019 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genetic Programming [[electronic resource] ] : 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings / / edited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez
| Genetic Programming [[electronic resource] ] : 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings / / edited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XII, 323 p. 80 illus.) |
| Disciplina | 006.31 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Computer arithmetic and logic units Artificial intelligence Data mining Data structures (Computer science) Information theory Arithmetic and Logic Structures Artificial Intelligence Data Mining and Knowledge Discovery Data Structures and Information Theory |
| ISBN | 3-319-77553-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Using GP Is NEAT: Evolving Compositional Pattern Production Functions -- Evolving the Topology of Large Scale Deep Neural Networks -- Evolving Graphs by Graph Programming -- Pruning Techniques for Mixed Ensembles of Genetic Programming Models -- Analyzing Feature Importance for Metabolomics Using Genetic Programming -- Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming -- On the Automatic Design of a Representation for Grammar-Based Genetic Programming -- Multi-Level Grammar Genetic Programming for Scheduling in Heterogeneous Networks -- Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom -- Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States -- A Multiple Expression Alignment Framework for Genetic Programming -- Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions -- A Comparative Study on Crossover in Cartesian Genetic Programming -- Evolving Better RNAfold Structure Prediction -- Geometric Crossover in Syntactic Space -- Investigating A Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling -- Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming -- Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web -- Genetic Programming Hyperheuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling. . |
| Record Nr. | UNISA-996465757303316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Genetic Programming : 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings / / edited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez
| Genetic Programming : 21st European Conference, EuroGP 2018, Parma, Italy, April 4-6, 2018, Proceedings / / edited by Mauro Castelli, Lukas Sekanina, Mengjie Zhang, Stefano Cagnoni, Pablo García-Sánchez |
| Edizione | [1st ed. 2018.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 |
| Descrizione fisica | 1 online resource (XII, 323 p. 80 illus.) |
| Disciplina | 006.31 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Computer arithmetic and logic units Artificial intelligence Data mining Data structures (Computer science) Information theory Arithmetic and Logic Structures Artificial Intelligence Data Mining and Knowledge Discovery Data Structures and Information Theory |
| ISBN | 3-319-77553-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Using GP Is NEAT: Evolving Compositional Pattern Production Functions -- Evolving the Topology of Large Scale Deep Neural Networks -- Evolving Graphs by Graph Programming -- Pruning Techniques for Mixed Ensembles of Genetic Programming Models -- Analyzing Feature Importance for Metabolomics Using Genetic Programming -- Generating Redundant Features with Unsupervised Multi-Tree Genetic Programming -- On the Automatic Design of a Representation for Grammar-Based Genetic Programming -- Multi-Level Grammar Genetic Programming for Scheduling in Heterogeneous Networks -- Scaling Tangled Program Graphs to Visual Reinforcement Learning in ViZDoom -- Towards In Vivo Genetic Programming: Evolving Boolean Networks to Determine Cell States -- A Multiple Expression Alignment Framework for Genetic Programming -- Multi-Objective Evolution of Ultra-Fast General-Purpose Hash Functions -- A Comparative Study on Crossover in Cartesian Genetic Programming -- Evolving Better RNAfold Structure Prediction -- Geometric Crossover in Syntactic Space -- Investigating A Machine Breakdown Genetic Programming Approach for Dynamic Job Shop Scheduling -- Structurally Layered Representation Learning: Towards Deep Learning Through Genetic Programming -- Comparing Rule Evaluation Metrics for the Evolutionary Discovery of Multi-Relational Association Rules in the Semantic Web -- Genetic Programming Hyperheuristic with Cooperative Coevolution for Dynamic Flexible Job Shop Scheduling. . |
| Record Nr. | UNINA-9910349457603321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2018 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genetic Programming [[electronic resource] ] : 20th European Conference, EuroGP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings / / edited by James McDermott, Mauro Castelli, Lukas Sekanina, Evert Haasdijk, Pablo García-Sánchez
| Genetic Programming [[electronic resource] ] : 20th European Conference, EuroGP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings / / edited by James McDermott, Mauro Castelli, Lukas Sekanina, Evert Haasdijk, Pablo García-Sánchez |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XII, 359 p. 116 illus.) |
| Disciplina | 006.31 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Computer science Artificial intelligence Application software Computer science—Mathematics Discrete mathematics Theory of Computation Artificial Intelligence Computer and Information Systems Applications Discrete Mathematics in Computer Science |
| ISBN | 3-319-55696-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Oral Presentations -- Evolutionary Program Sketching -- 1 Introduction -- 2 Program Sketching -- 3 Evolutionary Program Sketching -- 3.1 Problem Specification -- 3.2 Instruction Set -- 3.3 Fitness Function -- 3.4 Exploiting the Feedback from Hole Completion -- 4 Related Work -- 5 Experimental Evaluation -- 6 Discussion -- 7 Conclusion -- References -- Exploring Fitness and Edit Distance of Mutated Python Programs -- 1 Introduction -- 2 Related Work -- 3 Our Implementation of Genetic Improvement -- 3.1 Fitness Function -- 3.2 Search Algorithm -- 4 Experimental Setup -- 4.1 Description of the Programs Targeted by GI -- 5 Results -- 5.1 Change in Fitness -- 5.2 Average Fitness with Respect to Edit List Size -- 5.3 Discrete Steps in Fitness -- 6 Conclusions -- References -- Differentiable Genetic Programming -- 1 Introduction -- 2 Program Encoding -- 3 The Algebra of Truncated Polynomials -- 3.1 The Link to Taylor Polynomials -- 3.2 Non Rational Functions -- 4 Example of a dCGP -- 5 Learning Constants in Symbolic Regression -- 5.1 Ephemeral Constants Approach -- 5.2 Weighted dCGP Approach -- 6 Solution to Differential Equations -- 7 Discovery of Prime Integrals -- 8 Conclusions -- References -- Evolving Game State Features from Raw Pixels -- 1 Introduction -- 2 Related Research -- 3 Materials -- 3.1 Games -- 3.2 Handcrafted Game State Features -- 4 Evolving Video Game State Visual Features Using Genetic Programming -- 4.1 Evolving Game State Features -- 4.2 Voting for Actions -- 5 Results -- 6 Conclusion -- References -- Emergent Tangled Graph Representations for Atari Game Playing Agents -- 1 Introduction -- 2 Background -- 3 The Arcade Learning Environment -- 3.1 Screen State Space Representation -- 4 Evolving Tangled Program Graphs -- 4.1 Coevolving Teams of Programs -- 4.2 Emergent Modularity.
4.3 Diversity Maintenance -- 5 Empirical Experiments -- 5.1 Experimental Setup -- 5.2 Results -- 5.3 Solution Analysis -- 6 Conclusion and Future Work -- References -- A General Feature Engineering Wrapper for Machine Learning Using -Lexicase Survival -- 1 Introduction -- 2 Feature Engineering Wrapper -- 2.1 -lexicase Survival -- 2.2 Scaling -- 3 Related Work -- 4 Experimental Analysis -- 4.1 Problems -- 5 Results -- 5.1 Hyper-Parameter Optimization -- 5.2 Problem Performance -- 5.3 Statistical Analysis -- 6 Discussion -- 7 Conclusions -- References -- Visualising the Search Landscape of the Triangle Program -- 1 Genetic Improvement -- 2 Triangle Program Software Engineering Benchmark -- 3 Binary Representation: Replacing Comparisons with One Alternative -- 3.1 High Order Binary Schema Are Not Deceptive -- 3.2 Binary Schema Predict All Solutions of the Triangle Program -- 3.3 Local Search Landscape of the Binary Space -- 4 Original All Comparisons -- 4.1 Fitness Space of Triangle Program -- 4.2 High Order Schema Analysis -- 4.3 Local Search for the Triangle Program -- 4.4 Local Optima Networks -- 5 Conclusions -- References -- RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming -- 1 Introduction -- 2 Background -- 2.1 Outliers -- 3 Robust Regression -- 4 Proposed RANSAC-GP -- 4.1 Proposal -- 5 Experiments and Results -- 5.1 Results -- 6 Conclusion and Future Work -- References -- Symbolic Regression on Network Properties -- 1 Introduction -- 2 Related Work -- 2.1 Symbolic Regression -- 2.2 Cartesian Genetic Programming (CGP) -- 3 Networks -- 3.1 Network Representations -- 3.2 Network Properties -- 4 Experiments -- 4.1 Network Diameter -- 4.2 Isoperimetric Number -- 5 Discussion -- 5.1 Network Diameter -- 5.2 Isoperimetric Number -- 6 Conclusion -- References. Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming -- 1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Background -- 2.1 Job Shop Scheduling -- 2.2 Automatic Design of Dispatching Rules -- 3 Time-Invariant Dispatching Rule -- 3.1 An Example: Time-Invariance v.s. Time-Dependence -- 3.2 Relationship Between Existing Rule Classifications -- 4 Selection of Terminals for Time-Invariance -- 5 Experimental Studies -- 5.1 Results and Discussions -- 5.2 Further Analysis -- 5.3 Time-Invariance of the Evolved Rules -- 6 Conclusions and Future Work -- References -- Strategies for Improving the Distribution of Random Function Outputs in GSGP -- 1 Introduction -- 2 Background and Motivation -- 2.1 Geometric Semantic Genetic Programming -- 2.2 The Impact of the Random Functions -- 3 Related Work -- 4 Strategies for Normalizing Outputs of Random Functions -- 5 Experimental Analysis -- 5.1 Normalization Impact on the Distribution of the Semantics of Random Functions -- 5.2 The Impact on the GSGP Performance -- 6 Conclusions and Future Work -- References -- Synthesis of Mathematical Programming Constraints with Genetic Programming -- 1 Introduction -- 2 Related Work -- 3 Constraint Synthesis -- 3.1 Constraint Synthesis Problem -- 3.2 Genetic Constraint Synthesis (GenetiCS) -- 4 Experiment -- 4.1 Setup -- 4.2 Evaluation of GP Setups -- 4.3 Evaluation of Synthesized Models -- 5 Conclusion -- References -- Grammatical Evolution of Robust Controller Structures Using Wilson Scoring and Criticality Ranking -- 1 Introduction -- 2 Background -- 2.1 Grammatical Evolution -- 2.2 Robust Control -- 3 Methodology -- 3.1 General Process -- 3.2 Wilson Scoring -- 3.3 Criticality Ranking -- 4 Experiments -- 4.1 Benchmark Problem -- 4.2 Metrics and Setup -- 4.3 Results -- 5 Conclusion -- References. Using Feature Clustering for GP-Based Feature Construction on High-Dimensional Data -- 1 Introduction -- 2 Background -- 2.1 Genetic Programming for Feature Construction -- 2.2 Feature Clustering -- 3 The Proposed Approach -- 3.1 The Redundancy Based Feature Clustering Method: RFC -- 3.2 The Proposed Method: CGPFC -- 4 Experiment Design -- 5 Results and Discussions -- 5.1 Performance of the Constructed Feature -- 5.2 Performance of the Constructed and Selected Features -- 5.3 Cluster Analysis -- 6 Conclusions and Future Work -- References -- Posters -- Geometric Semantic Crossover with an Angle-Aware Mating Scheme in Genetic Programming for Symbolic Regression -- 1 Introduction -- 2 Background -- 2.1 Geometric Semantic GP -- 2.2 Locally Geometric Semantic Crossover -- 2.3 Related Work -- 3 Angle-Aware Geometric Semantic Crossover (AGSX) -- 3.1 Main Idea -- 3.2 The AGSX Process -- 3.3 Main Characteristics of AGSX -- 3.4 Fitness Function of the Algorithm -- 4 Experiments Setup -- 4.1 Benchmark Problems -- 4.2 Parameter Settings -- 5 Results and Discussions -- 5.1 Overall Results -- 5.2 Analysis on the Learning Performance -- 5.3 Analysis of the Evolution of Generalisation Performance -- 5.4 Analysis of the Angles -- 5.5 Comparison on Computational Time and Program Size -- 6 Conclusions and Future Work -- References -- RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines -- 1 Introduction -- 2 Related Work -- 3 Automatically Evolving Classification Pipelines -- 3.1 Grammar: Representing Effective Classification Pipelines -- 3.2 Individual Representation -- 3.3 Individual Evaluation -- 4 Experimental Results -- 4.1 Comparison with Other State-of-the-Art Methods -- 4.2 Analysis of the Evolutionary Process of RECIPE -- 5 Conclusions and Future Work -- References. A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming -- 1 Introduction -- 2 System Description -- 2.1 Grammar -- 2.2 Skeleton -- 2.3 Python Specific Differences -- 2.4 Implementation Details -- 3 Previous Approaches to Program Synthesis -- 3.1 PushGP -- 3.2 Strongly Formed Genetic Programming -- 3.3 Grammar Guided Genetic Programming -- 3.4 Program Synthesis via Code Reusage -- 3.5 Comparison of Program Synthesis Approaches -- 4 Experimental Setup -- 4.1 Benchmark Suite -- 4.2 Experimental Parameter Settings -- 4.3 PushGP Differences -- 5 Results -- 5.1 Comparison to PushGP on Tournament Selection -- 5.2 Comparison to PushGP on Lexicase Selection -- 5.3 Generational Progress -- 5.4 Invalids -- 6 Conclusion and Future Work -- References -- Improving the Tartarus Problem as a Benchmark in Genetic Programming -- 1 Introduction -- 2 Desirable GP Benchmark Characteristics -- 3 GP Benchmarks -- 3.1 The Lawnmower Problem -- 4 The Tartarus Problem -- 4.1 Satisfying the Desirable Benchmark Characteristics -- 4.2 Current State Evaluation -- 4.3 Proposed Improved State Evaluation -- 4.4 Baseline Values for Tartarus Instances -- 4.5 Generating Tartarus Instances -- 4.6 Tuning Difficulty -- 5 Conclusion -- References -- A New Subgraph Crossover for Cartesian Genetic Programming -- 1 Introduction -- 2 Related Work -- 2.1 Cartesian Genetic Programming -- 2.2 Previous Work on Crossover in CGP -- 3 The Proposed Method -- 3.1 Multiple Outputs -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Symbolic Regression -- 4.3 Boolean Functions -- 4.4 Image Operator Design -- 4.5 Crossover Comparison -- 5 Discussion -- 6 Conclusion and Future Work -- References -- A Comparative Study of Different Grammar-Based Genetic Programming Approaches -- 1 Introduction -- 2 Grammar-Based Genetic Programming. 2.1 Contex-Free-Grammar Genetic Programming (CFG-GP). |
| Record Nr. | UNISA-996466334203316 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Genetic Programming : 20th European Conference, EuroGP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings / / edited by James McDermott, Mauro Castelli, Lukas Sekanina, Evert Haasdijk, Pablo García-Sánchez
| Genetic Programming : 20th European Conference, EuroGP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings / / edited by James McDermott, Mauro Castelli, Lukas Sekanina, Evert Haasdijk, Pablo García-Sánchez |
| Edizione | [1st ed. 2017.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 |
| Descrizione fisica | 1 online resource (XII, 359 p. 116 illus.) |
| Disciplina | 006.31 |
| Collana | Theoretical Computer Science and General Issues |
| Soggetto topico |
Algorithms
Computer science Artificial intelligence Application software Computer science—Mathematics Discrete mathematics Theory of Computation Artificial Intelligence Computer and Information Systems Applications Discrete Mathematics in Computer Science |
| ISBN | 3-319-55696-7 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Oral Presentations -- Evolutionary Program Sketching -- 1 Introduction -- 2 Program Sketching -- 3 Evolutionary Program Sketching -- 3.1 Problem Specification -- 3.2 Instruction Set -- 3.3 Fitness Function -- 3.4 Exploiting the Feedback from Hole Completion -- 4 Related Work -- 5 Experimental Evaluation -- 6 Discussion -- 7 Conclusion -- References -- Exploring Fitness and Edit Distance of Mutated Python Programs -- 1 Introduction -- 2 Related Work -- 3 Our Implementation of Genetic Improvement -- 3.1 Fitness Function -- 3.2 Search Algorithm -- 4 Experimental Setup -- 4.1 Description of the Programs Targeted by GI -- 5 Results -- 5.1 Change in Fitness -- 5.2 Average Fitness with Respect to Edit List Size -- 5.3 Discrete Steps in Fitness -- 6 Conclusions -- References -- Differentiable Genetic Programming -- 1 Introduction -- 2 Program Encoding -- 3 The Algebra of Truncated Polynomials -- 3.1 The Link to Taylor Polynomials -- 3.2 Non Rational Functions -- 4 Example of a dCGP -- 5 Learning Constants in Symbolic Regression -- 5.1 Ephemeral Constants Approach -- 5.2 Weighted dCGP Approach -- 6 Solution to Differential Equations -- 7 Discovery of Prime Integrals -- 8 Conclusions -- References -- Evolving Game State Features from Raw Pixels -- 1 Introduction -- 2 Related Research -- 3 Materials -- 3.1 Games -- 3.2 Handcrafted Game State Features -- 4 Evolving Video Game State Visual Features Using Genetic Programming -- 4.1 Evolving Game State Features -- 4.2 Voting for Actions -- 5 Results -- 6 Conclusion -- References -- Emergent Tangled Graph Representations for Atari Game Playing Agents -- 1 Introduction -- 2 Background -- 3 The Arcade Learning Environment -- 3.1 Screen State Space Representation -- 4 Evolving Tangled Program Graphs -- 4.1 Coevolving Teams of Programs -- 4.2 Emergent Modularity.
4.3 Diversity Maintenance -- 5 Empirical Experiments -- 5.1 Experimental Setup -- 5.2 Results -- 5.3 Solution Analysis -- 6 Conclusion and Future Work -- References -- A General Feature Engineering Wrapper for Machine Learning Using -Lexicase Survival -- 1 Introduction -- 2 Feature Engineering Wrapper -- 2.1 -lexicase Survival -- 2.2 Scaling -- 3 Related Work -- 4 Experimental Analysis -- 4.1 Problems -- 5 Results -- 5.1 Hyper-Parameter Optimization -- 5.2 Problem Performance -- 5.3 Statistical Analysis -- 6 Discussion -- 7 Conclusions -- References -- Visualising the Search Landscape of the Triangle Program -- 1 Genetic Improvement -- 2 Triangle Program Software Engineering Benchmark -- 3 Binary Representation: Replacing Comparisons with One Alternative -- 3.1 High Order Binary Schema Are Not Deceptive -- 3.2 Binary Schema Predict All Solutions of the Triangle Program -- 3.3 Local Search Landscape of the Binary Space -- 4 Original All Comparisons -- 4.1 Fitness Space of Triangle Program -- 4.2 High Order Schema Analysis -- 4.3 Local Search for the Triangle Program -- 4.4 Local Optima Networks -- 5 Conclusions -- References -- RANSAC-GP: Dealing with Outliers in Symbolic Regression with Genetic Programming -- 1 Introduction -- 2 Background -- 2.1 Outliers -- 3 Robust Regression -- 4 Proposed RANSAC-GP -- 4.1 Proposal -- 5 Experiments and Results -- 5.1 Results -- 6 Conclusion and Future Work -- References -- Symbolic Regression on Network Properties -- 1 Introduction -- 2 Related Work -- 2.1 Symbolic Regression -- 2.2 Cartesian Genetic Programming (CGP) -- 3 Networks -- 3.1 Network Representations -- 3.2 Network Properties -- 4 Experiments -- 4.1 Network Diameter -- 4.2 Isoperimetric Number -- 5 Discussion -- 5.1 Network Diameter -- 5.2 Isoperimetric Number -- 6 Conclusion -- References. Evolving Time-Invariant Dispatching Rules in Job Shop Scheduling with Genetic Programming -- 1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Background -- 2.1 Job Shop Scheduling -- 2.2 Automatic Design of Dispatching Rules -- 3 Time-Invariant Dispatching Rule -- 3.1 An Example: Time-Invariance v.s. Time-Dependence -- 3.2 Relationship Between Existing Rule Classifications -- 4 Selection of Terminals for Time-Invariance -- 5 Experimental Studies -- 5.1 Results and Discussions -- 5.2 Further Analysis -- 5.3 Time-Invariance of the Evolved Rules -- 6 Conclusions and Future Work -- References -- Strategies for Improving the Distribution of Random Function Outputs in GSGP -- 1 Introduction -- 2 Background and Motivation -- 2.1 Geometric Semantic Genetic Programming -- 2.2 The Impact of the Random Functions -- 3 Related Work -- 4 Strategies for Normalizing Outputs of Random Functions -- 5 Experimental Analysis -- 5.1 Normalization Impact on the Distribution of the Semantics of Random Functions -- 5.2 The Impact on the GSGP Performance -- 6 Conclusions and Future Work -- References -- Synthesis of Mathematical Programming Constraints with Genetic Programming -- 1 Introduction -- 2 Related Work -- 3 Constraint Synthesis -- 3.1 Constraint Synthesis Problem -- 3.2 Genetic Constraint Synthesis (GenetiCS) -- 4 Experiment -- 4.1 Setup -- 4.2 Evaluation of GP Setups -- 4.3 Evaluation of Synthesized Models -- 5 Conclusion -- References -- Grammatical Evolution of Robust Controller Structures Using Wilson Scoring and Criticality Ranking -- 1 Introduction -- 2 Background -- 2.1 Grammatical Evolution -- 2.2 Robust Control -- 3 Methodology -- 3.1 General Process -- 3.2 Wilson Scoring -- 3.3 Criticality Ranking -- 4 Experiments -- 4.1 Benchmark Problem -- 4.2 Metrics and Setup -- 4.3 Results -- 5 Conclusion -- References. Using Feature Clustering for GP-Based Feature Construction on High-Dimensional Data -- 1 Introduction -- 2 Background -- 2.1 Genetic Programming for Feature Construction -- 2.2 Feature Clustering -- 3 The Proposed Approach -- 3.1 The Redundancy Based Feature Clustering Method: RFC -- 3.2 The Proposed Method: CGPFC -- 4 Experiment Design -- 5 Results and Discussions -- 5.1 Performance of the Constructed Feature -- 5.2 Performance of the Constructed and Selected Features -- 5.3 Cluster Analysis -- 6 Conclusions and Future Work -- References -- Posters -- Geometric Semantic Crossover with an Angle-Aware Mating Scheme in Genetic Programming for Symbolic Regression -- 1 Introduction -- 2 Background -- 2.1 Geometric Semantic GP -- 2.2 Locally Geometric Semantic Crossover -- 2.3 Related Work -- 3 Angle-Aware Geometric Semantic Crossover (AGSX) -- 3.1 Main Idea -- 3.2 The AGSX Process -- 3.3 Main Characteristics of AGSX -- 3.4 Fitness Function of the Algorithm -- 4 Experiments Setup -- 4.1 Benchmark Problems -- 4.2 Parameter Settings -- 5 Results and Discussions -- 5.1 Overall Results -- 5.2 Analysis on the Learning Performance -- 5.3 Analysis of the Evolution of Generalisation Performance -- 5.4 Analysis of the Angles -- 5.5 Comparison on Computational Time and Program Size -- 6 Conclusions and Future Work -- References -- RECIPE: A Grammar-Based Framework for Automatically Evolving Classification Pipelines -- 1 Introduction -- 2 Related Work -- 3 Automatically Evolving Classification Pipelines -- 3.1 Grammar: Representing Effective Classification Pipelines -- 3.2 Individual Representation -- 3.3 Individual Evaluation -- 4 Experimental Results -- 4.1 Comparison with Other State-of-the-Art Methods -- 4.2 Analysis of the Evolutionary Process of RECIPE -- 5 Conclusions and Future Work -- References. A Grammar Design Pattern for Arbitrary Program Synthesis Problems in Genetic Programming -- 1 Introduction -- 2 System Description -- 2.1 Grammar -- 2.2 Skeleton -- 2.3 Python Specific Differences -- 2.4 Implementation Details -- 3 Previous Approaches to Program Synthesis -- 3.1 PushGP -- 3.2 Strongly Formed Genetic Programming -- 3.3 Grammar Guided Genetic Programming -- 3.4 Program Synthesis via Code Reusage -- 3.5 Comparison of Program Synthesis Approaches -- 4 Experimental Setup -- 4.1 Benchmark Suite -- 4.2 Experimental Parameter Settings -- 4.3 PushGP Differences -- 5 Results -- 5.1 Comparison to PushGP on Tournament Selection -- 5.2 Comparison to PushGP on Lexicase Selection -- 5.3 Generational Progress -- 5.4 Invalids -- 6 Conclusion and Future Work -- References -- Improving the Tartarus Problem as a Benchmark in Genetic Programming -- 1 Introduction -- 2 Desirable GP Benchmark Characteristics -- 3 GP Benchmarks -- 3.1 The Lawnmower Problem -- 4 The Tartarus Problem -- 4.1 Satisfying the Desirable Benchmark Characteristics -- 4.2 Current State Evaluation -- 4.3 Proposed Improved State Evaluation -- 4.4 Baseline Values for Tartarus Instances -- 4.5 Generating Tartarus Instances -- 4.6 Tuning Difficulty -- 5 Conclusion -- References -- A New Subgraph Crossover for Cartesian Genetic Programming -- 1 Introduction -- 2 Related Work -- 2.1 Cartesian Genetic Programming -- 2.2 Previous Work on Crossover in CGP -- 3 The Proposed Method -- 3.1 Multiple Outputs -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Symbolic Regression -- 4.3 Boolean Functions -- 4.4 Image Operator Design -- 4.5 Crossover Comparison -- 5 Discussion -- 6 Conclusion and Future Work -- References -- A Comparative Study of Different Grammar-Based Genetic Programming Approaches -- 1 Introduction -- 2 Grammar-Based Genetic Programming. 2.1 Contex-Free-Grammar Genetic Programming (CFG-GP). |
| Record Nr. | UNINA-9910483925703321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2017 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Mathematical and Engineering Methods in Computer Science [[electronic resource] ] : 7th International Doctoral Workshop, MEMICS 2011, Lednice, Czech Republic, October 14-16, 2011, Revised Selected Papers / / edited by Zdeněk Kotásek, Jan Bouda, Ivana Cerná, Lukas Sekanina, Tomas Vojnar, David Antoš
| Mathematical and Engineering Methods in Computer Science [[electronic resource] ] : 7th International Doctoral Workshop, MEMICS 2011, Lednice, Czech Republic, October 14-16, 2011, Revised Selected Papers / / edited by Zdeněk Kotásek, Jan Bouda, Ivana Cerná, Lukas Sekanina, Tomas Vojnar, David Antoš |
| Edizione | [1st ed. 2012.] |
| Pubbl/distr/stampa | Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 |
| Descrizione fisica | 1 online resource (XII, 215 p. 53 illus.) |
| Disciplina | 004 |
| Collana | Programming and Software Engineering |
| Soggetto topico |
Computer communication systems
Algorithms Software engineering Management information systems Computer science Computer logic Computer Communication Networks Algorithm Analysis and Problem Complexity Software Engineering Management of Computing and Information Systems Logics and Meanings of Programs |
| Soggetto genere / forma |
Kongress2011.Lednice
Conference proceedings. |
| ISBN | 3-642-25929-4 |
| Classificazione |
004
SS 4800 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996465934703316 |
| Berlin, Heidelberg : , : Springer Berlin Heidelberg : , : Imprint : Springer, , 2012 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||