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Genetic programming : 25th European Conference, EuroGP 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings / / Eric Medvet, Gisele Pappa, and Bing Xue
| Genetic programming : 25th European Conference, EuroGP 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings / / Eric Medvet, Gisele Pappa, and Bing Xue |
| Autore | Medvet Eric |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Genetic programming (Computer science) |
| ISBN | 3-031-02056-1 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996472069903316 |
Medvet Eric
|
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| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Genetic programming : 25th European Conference, EuroGP 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings / / Eric Medvet, Gisele Pappa, and Bing Xue
| Genetic programming : 25th European Conference, EuroGP 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, Proceedings / / Eric Medvet, Gisele Pappa, and Bing Xue |
| Autore | Medvet Eric |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer International Publishing, , [2022] |
| Descrizione fisica | 1 online resource (317 pages) |
| Disciplina | 006.31 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Genetic programming (Computer science) |
| ISBN | 3-031-02056-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNINA-9910564687603321 |
|
Medvet Eric
|
||
| Cham, Switzerland : , : Springer International Publishing, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Genetic programming : 26th European conference, EuroGP 2023, held as part of EvoStar 2023, Brno, Czech Republic, April 12-14, 2023, proceedings / / edited by Gisele Pappa, Mario Giacobini, and Zdenek Vasicek
| Genetic programming : 26th European conference, EuroGP 2023, held as part of EvoStar 2023, Brno, Czech Republic, April 12-14, 2023, proceedings / / edited by Gisele Pappa, Mario Giacobini, and Zdenek Vasicek |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (366 pages) |
| Disciplina | 304.2 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Genetic programming (Computer science) |
| ISBN |
9783031295737
9783031295720 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Long Presentations -- A Self-Adaptive Approach to Exploit Topological Properties of Different GAs' Crossover Operators -- 1 Introduction -- 2 Fundamental Concepts -- 2.1 Crossover -- 2.2 Convex Combination, Convex Hull, and Convex Search -- 3 Related Works -- 4 Methodology -- 4.1 Dynamic Diversity Maintenance -- 4.2 Self-adaptive Crossover -- 5 Experimental Settings -- 6 Experimental Results -- 7 Conclusions -- References -- A Genetic Programming Encoder for Increasing Autoencoder Interpretability -- 1 Introduction -- 1.1 Structure -- 2 Background and Related Work -- 2.1 Non-linear Dimensionality Reduction -- 2.2 Evolutionary Computation for Dimensionality Reduction -- 2.3 Genetic Programming for Autoencoding -- 3 Proposed Method: GPE-AE -- 3.1 GP Representation of Encoder -- 3.2 Fitness Evaluation -- 3.3 Decoder Architecture -- 4 Experiment Design -- 4.1 Comparison Methods -- 4.2 Evaluation Measures -- 4.3 Datasets -- 5 Results -- 6 Further Analysis -- 7 Conclusions -- References -- Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows -- 1 Introduction -- 2 Background and Related Work -- 2.1 Genetic Programming in Physics Applications -- 2.2 Machine Learning for Particle-Laden Flows -- 3 Proposed Methods -- 3.1 Graph Networks -- 3.2 Genetic Programming -- 4 Experiment Design -- 4.1 Data Generation: Simulation of Particle-Laden Flows -- 4.2 Data Preprocessing -- 4.3 Algorithm Settings -- 5 Results and Analysis -- 5.1 Overall Algorithm Performance -- 5.2 Explainability of Equations -- 5.3 Validation of Symbolic Models -- 6 Conclusion and Future Work -- References -- Phenotype Search Trajectory Networks for Linear Genetic Programming -- 1 Introduction -- 2 The LGP System -- 2.1 Boolean LGP Algorithm -- 2.2 Genotype, Phenotype, and Fitness.
3 Kolmogorov Complexity -- 4 Sampling and Metrics Estimation -- 5 Search Trajectory Networks -- 5.1 General Definitions -- 5.2 The Proposed STN Models -- 5.3 Network Visualisation -- 5.4 Comparing Three Targets with Increasing Difficulty -- 6 Discussion -- References -- GPAM: Genetic Programming with Associative Memory -- 1 Introduction -- 2 Related Work -- 2.1 Symbolic Regression and Genetic Programming -- 2.2 Efficient Processing of DNNs -- 2.3 Weight Compression -- 3 Proposed Method -- 3.1 The GPAM Approach -- 3.2 GPAM for Weight Generation -- 4 Results for Symbolic Regression Benchmarks -- 4.1 Benchmarks -- 4.2 Setup -- 4.3 Memory Sizing -- 4.4 Role of Constants in GPAM -- 5 Results for Weight Generation -- 6 Discussion and Conclusions -- References -- MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning -- 1 Introduction -- 2 Related Work -- 2.1 Semantic GP -- 2.2 GP-Based Ensemble Learning -- 2.3 Quality Diversity Optimization -- 3 The Proposed Ensemble Learning Algorithm -- 3.1 The Overall Framework -- 3.2 Angle-Based Dimensionality Reduction -- 3.3 Reference Semantic Points -- 4 Experiment Settings -- 4.1 Datasets -- 4.2 Experimental Protocol -- 4.3 Parameter Settings -- 4.4 Benchmark Dimensionality Reduction Methods -- 5 Experimental Results -- 5.1 Comparisons of MAP-Elites Using Different Dimensionality Reduction Methods -- 5.2 Impact of Using Reference Points -- 5.3 Comparison with Other Machine Learning and Symbolic Regression Methods -- 6 Conclusions -- References -- Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming -- 1 Introduction -- 2 Related Work -- 3 Denoising Autoencoder LSTM -- 3.1 Model Building and Sampling -- 3.2 A New Denoising Strategy: Levenshtein Tree Edit -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Prediction Quality. 4.3 Analyzing the Search Behavior -- 5 Conclusions and Future Work -- References -- Context Matters: Adaptive Mutation for Grammars -- 1 Introduction -- 2 Background -- 2.1 Grammar-Based Genetic Programming -- 2.2 Adaptive Mutation Rate -- 2.3 Grammar-Design -- 3 Adaptive Facilitated Mutation -- 3.1 Grammar Design for Adaptive Facilitated Mutation -- 4 Experimental Setup -- 5 Results -- 6 Conclusion -- 6.1 Future Work -- References -- A Boosting Approach to Constructing an Ensemble Stack -- 1 Introduction -- 2 Related Work -- 3 Evolving an Ensemble Stack Using Boosting -- 3.1 The Boosting Ensemble Stack Algorithm -- 3.2 Evaluating an Ensemble Stack Post Training -- 3.3 Using an Extremely Large Number of Bins -- 4 Experimental Methodology -- 5 Results -- 5.1 Small Scale Classification Tasks -- 5.2 Large Scale Classification Task -- 6 Conclusion -- References -- Adaptive Batch Size CGP: Improving Accuracy and Runtime for CGP Logic Optimization Flow -- 1 Introduction -- 2 Cartesian Genetic Programming -- 2.1 Representation -- 2.2 Evolutionary Process -- 3 Methodology -- 3.1 Definitions -- 3.2 Adaptive Batch Size CGP -- 3.3 Experimental Protocol -- 4 Results -- 5 Conclusion -- References -- Faster Convergence with Lexicase Selection in Tree-Based Automated Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Review of TPOT -- 3.2 Parent Selection Algorithms -- 4 Experimental Set-Up -- 4.1 Datasets -- 4.2 Implementation -- 4.3 Evaluating Convergence -- 4.4 Exploration of Pipelines -- 5 Results -- 5.1 DIGEN Datasets -- 5.2 ANGES Datasets -- 6 Discussion -- References -- Using FPGA Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies -- 1 Introduction -- 2 Related Work -- 3 Accelerator Architecture -- 3.1 Program Memory -- 3.2 Program Compiler -- 3.3 Program Evaluator. 4 Design of Experiments -- 4.1 Comparison Metrics -- 4.2 Primitive Sets -- 4.3 Program Generation -- 4.4 Fitness Cases -- 5 Results -- 6 Current Limitations and Potential Optimizations -- 6.1 Current Limitations -- 6.2 Potential Optimizations -- 7 Conclusion -- References -- Memetic Semantic Genetic Programming for Symbolic Regression -- 1 Introduction -- 2 Semantic GP -- 2.1 Library Building and Searching -- 3 Memetic Algorithms -- 4 Memetic Semantic for Symbolic Regression -- 4.1 Algorithm -- 4.2 Local Tree Improvement -- 5 Experimental Setup -- 6 Results -- 7 Related Work -- 8 Conclusion -- References -- Grammatical Evolution with Code2vec -- 1 Introduction -- 2 Background -- 2.1 Grammatical Evolution -- 2.2 Code2vec -- 3 Methods -- 3.1 ClusterBooster -- 3.2 ClusterSelection -- 4 Experiments -- 4.1 Benchmarks -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Short Presentations -- Domain-Aware Feature Learning with Grammar-Guided Genetic Programming -- 1 Introduction -- 2 Related Work -- 2.1 Genetic-Programming-Based Feature Learning -- 2.2 Domain-Aware Feature Learning and Aggregation Incorporation -- 3 Method -- 3.1 Domain Knowledge M3GP -- 3.2 Domain Knowledge and Aggregation M3GP -- 4 Evaluation -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experiment Details -- 5 Results -- 6 Conclusion -- References -- Genetic Improvement of LLVM Intermediate Representation -- 1 Introduction -- 2 Background -- 3 Mutating LLVM IR -- 3.1 Representation -- 3.2 LLVM IR define Functions -- 3.3 Mutable LLVM IR -- 3.4 Compiling C/C++ etc. to Generate LLVM IR -- 3.5 Selecting Which LLVM IR to Optimise -- 3.6 Deleting LLVM IR -- 4 Fitness Function -- 4.1 Test Cases for Google's OLC and Uber's H3: GB Post Codes -- 4.2 Counting Instructions with perf stat -e instructions -x, -- 4.3 Sandboxing to Prevent Running Mutations Causing Harm. 4.4 Timeouts to Stop Poor Mutants Delaying Search -- 4.5 Limiting Output Size to Avoid Filling Disk or Exceeding Disk Quota -- 5 Hillclimbing Search -- 6 Results -- 7 Discussion -- 7.1 Types of Improvement Found -- 7.2 Discussion: Future Work, Co-evolution, Perf, Fitness Landscape -- 8 Conclusions -- References -- Spatial Genetic Programming -- 1 Introduction -- 2 Related Literature -- 3 Spatial Genetic Programming -- 3.1 The Cost Function -- 3.2 Outputs, Termination Conditions and Model Execution -- 3.3 Evolution of Models and the Genetic Operators -- 3.4 Conditional Return Statements -- 4 Experiments and Results -- 4.1 Case Study: Classic Control Problems -- 4.2 Case Study: Custom Toy Problems -- 4.3 Impact of a Spatial Crossover on the Evolution of Programs -- 5 Conclusion -- References -- All You Need is Sex for Diversity -- 1 Introduction -- 2 The PIMP Approach -- 3 Motivation -- 4 Methodology -- 4.1 Measures -- 4.2 Statistical Tests -- 5 Results -- 6 Discussion and Additional Remarks -- 7 Conclusion -- References -- On the Effects of Collaborators Selection and Aggregation in Cooperative Coevolution: An Experimental Analysis -- 1 Introduction and Related Works -- 2 A General Scheme for CC -- 3 Case Studies -- 3.1 Toy Problems -- 3.2 Symbolic Regression -- 3.3 Neuroevolution -- 4 Experimental Analysis -- 4.1 Toy Problems -- 4.2 Symbolic Regression -- 4.3 Neuroevolution -- 5 Concluding Remarks -- References -- To Bias or Not to Bias: Probabilistic Initialisation for Evolving Dispatching Rules -- 1 Introduction -- 2 Background -- 2.1 Unrelated Machines Environment -- 2.2 Designing Dispatching Rules with Genetic Programming -- 3 Probabilistic Individual Initialisation -- 4 Experimental Analysis -- 4.1 Benchmark Setup -- 4.2 Results -- 5 Analysis -- 5.1 Node Probabilities -- 5.2 Method Ranking -- 6 Conclusion -- References. MTGP: Combining Metamorphic Testing and Genetic Programming. |
| Record Nr. | UNISA-996517751603316 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Genetic programming : 26th European conference, EuroGP 2023, held as part of EvoStar 2023, Brno, Czech Republic, April 12-14, 2023, proceedings / / edited by Gisele Pappa, Mario Giacobini, and Zdenek Vasicek
| Genetic programming : 26th European conference, EuroGP 2023, held as part of EvoStar 2023, Brno, Czech Republic, April 12-14, 2023, proceedings / / edited by Gisele Pappa, Mario Giacobini, and Zdenek Vasicek |
| Edizione | [1st ed. 2023.] |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2023] |
| Descrizione fisica | 1 online resource (366 pages) |
| Disciplina | 304.2 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Genetic programming (Computer science) |
| ISBN |
9783031295737
9783031295720 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Long Presentations -- A Self-Adaptive Approach to Exploit Topological Properties of Different GAs' Crossover Operators -- 1 Introduction -- 2 Fundamental Concepts -- 2.1 Crossover -- 2.2 Convex Combination, Convex Hull, and Convex Search -- 3 Related Works -- 4 Methodology -- 4.1 Dynamic Diversity Maintenance -- 4.2 Self-adaptive Crossover -- 5 Experimental Settings -- 6 Experimental Results -- 7 Conclusions -- References -- A Genetic Programming Encoder for Increasing Autoencoder Interpretability -- 1 Introduction -- 1.1 Structure -- 2 Background and Related Work -- 2.1 Non-linear Dimensionality Reduction -- 2.2 Evolutionary Computation for Dimensionality Reduction -- 2.3 Genetic Programming for Autoencoding -- 3 Proposed Method: GPE-AE -- 3.1 GP Representation of Encoder -- 3.2 Fitness Evaluation -- 3.3 Decoder Architecture -- 4 Experiment Design -- 4.1 Comparison Methods -- 4.2 Evaluation Measures -- 4.3 Datasets -- 5 Results -- 6 Further Analysis -- 7 Conclusions -- References -- Graph Networks as Inductive Bias for Genetic Programming: Symbolic Models for Particle-Laden Flows -- 1 Introduction -- 2 Background and Related Work -- 2.1 Genetic Programming in Physics Applications -- 2.2 Machine Learning for Particle-Laden Flows -- 3 Proposed Methods -- 3.1 Graph Networks -- 3.2 Genetic Programming -- 4 Experiment Design -- 4.1 Data Generation: Simulation of Particle-Laden Flows -- 4.2 Data Preprocessing -- 4.3 Algorithm Settings -- 5 Results and Analysis -- 5.1 Overall Algorithm Performance -- 5.2 Explainability of Equations -- 5.3 Validation of Symbolic Models -- 6 Conclusion and Future Work -- References -- Phenotype Search Trajectory Networks for Linear Genetic Programming -- 1 Introduction -- 2 The LGP System -- 2.1 Boolean LGP Algorithm -- 2.2 Genotype, Phenotype, and Fitness.
3 Kolmogorov Complexity -- 4 Sampling and Metrics Estimation -- 5 Search Trajectory Networks -- 5.1 General Definitions -- 5.2 The Proposed STN Models -- 5.3 Network Visualisation -- 5.4 Comparing Three Targets with Increasing Difficulty -- 6 Discussion -- References -- GPAM: Genetic Programming with Associative Memory -- 1 Introduction -- 2 Related Work -- 2.1 Symbolic Regression and Genetic Programming -- 2.2 Efficient Processing of DNNs -- 2.3 Weight Compression -- 3 Proposed Method -- 3.1 The GPAM Approach -- 3.2 GPAM for Weight Generation -- 4 Results for Symbolic Regression Benchmarks -- 4.1 Benchmarks -- 4.2 Setup -- 4.3 Memory Sizing -- 4.4 Role of Constants in GPAM -- 5 Results for Weight Generation -- 6 Discussion and Conclusions -- References -- MAP-Elites with Cosine-Similarity for Evolutionary Ensemble Learning -- 1 Introduction -- 2 Related Work -- 2.1 Semantic GP -- 2.2 GP-Based Ensemble Learning -- 2.3 Quality Diversity Optimization -- 3 The Proposed Ensemble Learning Algorithm -- 3.1 The Overall Framework -- 3.2 Angle-Based Dimensionality Reduction -- 3.3 Reference Semantic Points -- 4 Experiment Settings -- 4.1 Datasets -- 4.2 Experimental Protocol -- 4.3 Parameter Settings -- 4.4 Benchmark Dimensionality Reduction Methods -- 5 Experimental Results -- 5.1 Comparisons of MAP-Elites Using Different Dimensionality Reduction Methods -- 5.2 Impact of Using Reference Points -- 5.3 Comparison with Other Machine Learning and Symbolic Regression Methods -- 6 Conclusions -- References -- Small Solutions for Real-World Symbolic Regression Using Denoising Autoencoder Genetic Programming -- 1 Introduction -- 2 Related Work -- 3 Denoising Autoencoder LSTM -- 3.1 Model Building and Sampling -- 3.2 A New Denoising Strategy: Levenshtein Tree Edit -- 4 Experiments -- 4.1 Experimental Setup -- 4.2 Prediction Quality. 4.3 Analyzing the Search Behavior -- 5 Conclusions and Future Work -- References -- Context Matters: Adaptive Mutation for Grammars -- 1 Introduction -- 2 Background -- 2.1 Grammar-Based Genetic Programming -- 2.2 Adaptive Mutation Rate -- 2.3 Grammar-Design -- 3 Adaptive Facilitated Mutation -- 3.1 Grammar Design for Adaptive Facilitated Mutation -- 4 Experimental Setup -- 5 Results -- 6 Conclusion -- 6.1 Future Work -- References -- A Boosting Approach to Constructing an Ensemble Stack -- 1 Introduction -- 2 Related Work -- 3 Evolving an Ensemble Stack Using Boosting -- 3.1 The Boosting Ensemble Stack Algorithm -- 3.2 Evaluating an Ensemble Stack Post Training -- 3.3 Using an Extremely Large Number of Bins -- 4 Experimental Methodology -- 5 Results -- 5.1 Small Scale Classification Tasks -- 5.2 Large Scale Classification Task -- 6 Conclusion -- References -- Adaptive Batch Size CGP: Improving Accuracy and Runtime for CGP Logic Optimization Flow -- 1 Introduction -- 2 Cartesian Genetic Programming -- 2.1 Representation -- 2.2 Evolutionary Process -- 3 Methodology -- 3.1 Definitions -- 3.2 Adaptive Batch Size CGP -- 3.3 Experimental Protocol -- 4 Results -- 5 Conclusion -- References -- Faster Convergence with Lexicase Selection in Tree-Based Automated Machine Learning -- 1 Introduction -- 2 Related Work -- 3 Methods -- 3.1 Review of TPOT -- 3.2 Parent Selection Algorithms -- 4 Experimental Set-Up -- 4.1 Datasets -- 4.2 Implementation -- 4.3 Evaluating Convergence -- 4.4 Exploration of Pipelines -- 5 Results -- 5.1 DIGEN Datasets -- 5.2 ANGES Datasets -- 6 Discussion -- References -- Using FPGA Devices to Accelerate Tree-Based Genetic Programming: A Preliminary Exploration with Recent Technologies -- 1 Introduction -- 2 Related Work -- 3 Accelerator Architecture -- 3.1 Program Memory -- 3.2 Program Compiler -- 3.3 Program Evaluator. 4 Design of Experiments -- 4.1 Comparison Metrics -- 4.2 Primitive Sets -- 4.3 Program Generation -- 4.4 Fitness Cases -- 5 Results -- 6 Current Limitations and Potential Optimizations -- 6.1 Current Limitations -- 6.2 Potential Optimizations -- 7 Conclusion -- References -- Memetic Semantic Genetic Programming for Symbolic Regression -- 1 Introduction -- 2 Semantic GP -- 2.1 Library Building and Searching -- 3 Memetic Algorithms -- 4 Memetic Semantic for Symbolic Regression -- 4.1 Algorithm -- 4.2 Local Tree Improvement -- 5 Experimental Setup -- 6 Results -- 7 Related Work -- 8 Conclusion -- References -- Grammatical Evolution with Code2vec -- 1 Introduction -- 2 Background -- 2.1 Grammatical Evolution -- 2.2 Code2vec -- 3 Methods -- 3.1 ClusterBooster -- 3.2 ClusterSelection -- 4 Experiments -- 4.1 Benchmarks -- 4.2 Experimental Setup -- 4.3 Results -- 5 Conclusion -- References -- Short Presentations -- Domain-Aware Feature Learning with Grammar-Guided Genetic Programming -- 1 Introduction -- 2 Related Work -- 2.1 Genetic-Programming-Based Feature Learning -- 2.2 Domain-Aware Feature Learning and Aggregation Incorporation -- 3 Method -- 3.1 Domain Knowledge M3GP -- 3.2 Domain Knowledge and Aggregation M3GP -- 4 Evaluation -- 4.1 Datasets -- 4.2 Implementation Details -- 4.3 Experiment Details -- 5 Results -- 6 Conclusion -- References -- Genetic Improvement of LLVM Intermediate Representation -- 1 Introduction -- 2 Background -- 3 Mutating LLVM IR -- 3.1 Representation -- 3.2 LLVM IR define Functions -- 3.3 Mutable LLVM IR -- 3.4 Compiling C/C++ etc. to Generate LLVM IR -- 3.5 Selecting Which LLVM IR to Optimise -- 3.6 Deleting LLVM IR -- 4 Fitness Function -- 4.1 Test Cases for Google's OLC and Uber's H3: GB Post Codes -- 4.2 Counting Instructions with perf stat -e instructions -x, -- 4.3 Sandboxing to Prevent Running Mutations Causing Harm. 4.4 Timeouts to Stop Poor Mutants Delaying Search -- 4.5 Limiting Output Size to Avoid Filling Disk or Exceeding Disk Quota -- 5 Hillclimbing Search -- 6 Results -- 7 Discussion -- 7.1 Types of Improvement Found -- 7.2 Discussion: Future Work, Co-evolution, Perf, Fitness Landscape -- 8 Conclusions -- References -- Spatial Genetic Programming -- 1 Introduction -- 2 Related Literature -- 3 Spatial Genetic Programming -- 3.1 The Cost Function -- 3.2 Outputs, Termination Conditions and Model Execution -- 3.3 Evolution of Models and the Genetic Operators -- 3.4 Conditional Return Statements -- 4 Experiments and Results -- 4.1 Case Study: Classic Control Problems -- 4.2 Case Study: Custom Toy Problems -- 4.3 Impact of a Spatial Crossover on the Evolution of Programs -- 5 Conclusion -- References -- All You Need is Sex for Diversity -- 1 Introduction -- 2 The PIMP Approach -- 3 Motivation -- 4 Methodology -- 4.1 Measures -- 4.2 Statistical Tests -- 5 Results -- 6 Discussion and Additional Remarks -- 7 Conclusion -- References -- On the Effects of Collaborators Selection and Aggregation in Cooperative Coevolution: An Experimental Analysis -- 1 Introduction and Related Works -- 2 A General Scheme for CC -- 3 Case Studies -- 3.1 Toy Problems -- 3.2 Symbolic Regression -- 3.3 Neuroevolution -- 4 Experimental Analysis -- 4.1 Toy Problems -- 4.2 Symbolic Regression -- 4.3 Neuroevolution -- 5 Concluding Remarks -- References -- To Bias or Not to Bias: Probabilistic Initialisation for Evolving Dispatching Rules -- 1 Introduction -- 2 Background -- 2.1 Unrelated Machines Environment -- 2.2 Designing Dispatching Rules with Genetic Programming -- 3 Probabilistic Individual Initialisation -- 4 Experimental Analysis -- 4.1 Benchmark Setup -- 4.2 Results -- 5 Analysis -- 5.1 Node Probabilities -- 5.2 Method Ranking -- 6 Conclusion -- References. MTGP: Combining Metamorphic Testing and Genetic Programming. |
| Record Nr. | UNINA-9910683354403321 |
| Cham, Switzerland : , : Springer, , [2023] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||