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2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) / / Institute of Electrical and Electronics Engineers (IEEE)
| 2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) / / Institute of Electrical and Electronics Engineers (IEEE) |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 |
| Descrizione fisica | 1 online resource : illustrations some color |
| Disciplina | 006.3823 |
| Soggetto topico |
Evolutionary computation
Conference papers and proceedings |
| ISBN | 1-7281-7553-4 |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2020 4th Conference on Swarm Intelligence and Evolutionary Computation |
| Record Nr. | UNINA-9910437250803321 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) / / Institute of Electrical and Electronics Engineers (IEEE)
| 2020 4th Conference on Swarm Intelligence and Evolutionary Computation (CSIEC) / / Institute of Electrical and Electronics Engineers (IEEE) |
| Pubbl/distr/stampa | Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 |
| Descrizione fisica | 1 online resource : illustrations some color |
| Disciplina | 006.3823 |
| Soggetto topico |
Evolutionary computation
Conference papers and proceedings |
| ISBN | 1-7281-7553-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Altri titoli varianti | 2020 4th Conference on Swarm Intelligence and Evolutionary Computation |
| Record Nr. | UNISA-996575649303316 |
| Piscataway, New Jersey : , : Institute of Electrical and Electronics Engineers (IEEE), , 2020 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
2022 IEEE Congress on Evolutionary Computation (CEC) / / Institute of Electrical and Electronics Engineers
| 2022 IEEE Congress on Evolutionary Computation (CEC) / / Institute of Electrical and Electronics Engineers |
| Pubbl/distr/stampa | [Place of publication not identified] : , : Institute of Electrical and Electronics Engineers, , 2022 |
| Descrizione fisica | 1 online resource |
| Disciplina | 006.3823 |
| Soggetto topico | Evolutionary computation |
| ISBN | 1-66546-708-8 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996574870003316 |
| [Place of publication not identified] : , : Institute of Electrical and Electronics Engineers, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
ACM transactions on evolutionary learning and optimization
| ACM transactions on evolutionary learning and optimization |
| Pubbl/distr/stampa | New York, NY : , : Association for Computing Machinery, , 2021- |
| Disciplina | 006.3823 |
| Soggetto topico |
Evolutionary computation
Evolutionary programming (Computer science) Réseaux neuronaux à structure évolutive Programmation évolutive |
| Soggetto genere / forma |
Periodicals.
periodicals. Périodiques. |
| ISSN | 2688-3007 |
| Formato | Materiale a stampa |
| Livello bibliografico | Periodico |
| Lingua di pubblicazione | eng |
| Altri titoli varianti |
ACM TELO
Association for Computing Machinery transactions on evolutionary learning and optimization |
| Record Nr. | UNINA-9910496496303321 |
| New York, NY : , : Association for Computing Machinery, , 2021- | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in computational particle based methods / / guest editors, Professor Y. T. Feng [and three others]
| Advances in computational particle based methods / / guest editors, Professor Y. T. Feng [and three others] |
| Pubbl/distr/stampa | [Bradford, England] : , : Emerald, , 2015 |
| Descrizione fisica | 1 online resource (259 p.) |
| Disciplina | 006.3823 |
| Collana | Engineering Computations : International journal for computer-aided engineering and software |
| Soggetto topico | Evolutionary computation |
| Soggetto genere / forma | Electronic books. |
| ISBN | 1-78560-195-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Editorial advisory board; Guest editorial; Multiscale hydro-mechanical analysis of unsaturated granular materials using bridging scale method; Multiscale properties of dense granular materials; Characteristic lengths in Cosserat continuum modeling of granular materials; DEM analyses of shear band in granular materials; A yield function for granular materials based on microstructures; Effects of density ratio and diameter ratio on penetration of rotation projectile obliquely impacting a granular medium
Numerical study of concrete mixing transport process and mixing mechanism of truck mixer Asymmetric local velocity distribution in a driven granular gas; 2D particle contact-based meshfree method in CDEM and its application in geotechnical problems; Discrete modeling of rockfill materials considering the irregular shaped particles and their crushability; Analysis of ice load on conical structure with discrete element method; Particles deposition on microfiltration permeable boundary; Numerical simulation of impinging jet flows by modified MPS method A comparative study of different baffles on mitigating liquids loshing in a rectangular tank due to a horizontal excitation |
| Record Nr. | UNINA-9910460639103321 |
| [Bradford, England] : , : Emerald, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in computational particle based methods / / guest editors, Professor Y. T. Feng [and three others]
| Advances in computational particle based methods / / guest editors, Professor Y. T. Feng [and three others] |
| Pubbl/distr/stampa | [Bradford, England] : , : Emerald, , 2015 |
| Descrizione fisica | 1 online resource (259 p.) |
| Disciplina | 006.3823 |
| Collana | Engineering Computations : International journal for computer-aided engineering and software |
| Soggetto topico | Evolutionary computation |
| ISBN | 1-78560-195-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Editorial advisory board; Guest editorial; Multiscale hydro-mechanical analysis of unsaturated granular materials using bridging scale method; Multiscale properties of dense granular materials; Characteristic lengths in Cosserat continuum modeling of granular materials; DEM analyses of shear band in granular materials; A yield function for granular materials based on microstructures; Effects of density ratio and diameter ratio on penetration of rotation projectile obliquely impacting a granular medium
Numerical study of concrete mixing transport process and mixing mechanism of truck mixer Asymmetric local velocity distribution in a driven granular gas; 2D particle contact-based meshfree method in CDEM and its application in geotechnical problems; Discrete modeling of rockfill materials considering the irregular shaped particles and their crushability; Analysis of ice load on conical structure with discrete element method; Particles deposition on microfiltration permeable boundary; Numerical simulation of impinging jet flows by modified MPS method A comparative study of different baffles on mitigating liquids loshing in a rectangular tank due to a horizontal excitation |
| Record Nr. | UNINA-9910797364403321 |
| [Bradford, England] : , : Emerald, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances in computational particle based methods / / guest editors, Professor Y. T. Feng [and three others]
| Advances in computational particle based methods / / guest editors, Professor Y. T. Feng [and three others] |
| Pubbl/distr/stampa | [Bradford, England] : , : Emerald, , 2015 |
| Descrizione fisica | 1 online resource (259 p.) |
| Disciplina | 006.3823 |
| Collana | Engineering Computations : International journal for computer-aided engineering and software |
| Soggetto topico | Evolutionary computation |
| ISBN | 1-78560-195-4 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Cover; Editorial advisory board; Guest editorial; Multiscale hydro-mechanical analysis of unsaturated granular materials using bridging scale method; Multiscale properties of dense granular materials; Characteristic lengths in Cosserat continuum modeling of granular materials; DEM analyses of shear band in granular materials; A yield function for granular materials based on microstructures; Effects of density ratio and diameter ratio on penetration of rotation projectile obliquely impacting a granular medium
Numerical study of concrete mixing transport process and mixing mechanism of truck mixer Asymmetric local velocity distribution in a driven granular gas; 2D particle contact-based meshfree method in CDEM and its application in geotechnical problems; Discrete modeling of rockfill materials considering the irregular shaped particles and their crushability; Analysis of ice load on conical structure with discrete element method; Particles deposition on microfiltration permeable boundary; Numerical simulation of impinging jet flows by modified MPS method A comparative study of different baffles on mitigating liquids loshing in a rectangular tank due to a horizontal excitation |
| Record Nr. | UNINA-9910819631603321 |
| [Bradford, England] : , : Emerald, , 2015 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Advances of Evolutionary Computation: Methods and Operators / / by Erik Cuevas, Margarita Arimatea Díaz Cortés, Diego Alberto Oliva Navarro
| Advances of Evolutionary Computation: Methods and Operators / / by Erik Cuevas, Margarita Arimatea Díaz Cortés, Diego Alberto Oliva Navarro |
| Autore | Cuevas Erik |
| Edizione | [1st ed. 2016.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 |
| Descrizione fisica | 1 online resource (XIV, 202 p. 48 illus., 43 illus. in color.) |
| Disciplina | 006.3823 |
| Collana | Studies in Computational Intelligence |
| Soggetto topico |
Computational intelligence
Artificial intelligence Computational Intelligence Artificial Intelligence |
| ISBN | 3-319-28503-3 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Introduction -- A Swarm Global Optimization Algorithm Inspired in the Behavior of the Social-spider.-A States of Matter Algorithm for Global Optimization -- An Algorithm for Global Optimization Inspired by Collective Animal Behavior -- A Bio-inspired Evolutionary Algorithm: Allostatic Optimization -- Optimization Based on the Behavior of Locust Swarms. . |
| Record Nr. | UNINA-9910254258203321 |
Cuevas Erik
|
||
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2016 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Applications of evolutionary computation : 25th European conference, EvoApplications 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings / / edited by Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, and Kehinde Oluwatoyin Babaagba
| Applications of evolutionary computation : 25th European conference, EvoApplications 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings / / edited by Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, and Kehinde Oluwatoyin Babaagba |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (759 pages) |
| Disciplina | 006.3823 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Evolutionary computation |
| ISBN | 3-031-02462-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Applications of Evolutionary Computation -- An Enhanced Opposition-Based Evolutionary Feature Selection Approach -- 1 Introduction -- 2 Moth Flame Optimization -- 2.1 Binary Moth Flame Optimization -- 2.2 Binary Moth Flame Optimization for Feature Selection -- 3 The Proposed Approach -- 3.1 Initialization Using Opposition-Based Method -- 3.2 Retiring Flame -- 4 Experimental Setup and Results -- 5 Conclusions -- References -- A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings -- 1 Introduction -- 2 Conductance-Based Model Description -- 3 Defining a Benchmark with Known Types of Ion Channels -- 4 Methodology and Experimental Setup -- 5 Experimental Results -- 6 Conclusions -- A Mathematical Description of the Models -- B Experimental Setup and Parameter Ranges -- References -- Swarm Optimised Few-View Binary Tomography -- 1 Introduction -- 2 Binary Tomographic Reconstruction -- 3 Swarm Optimisation -- 4 Constrained Search in High Dimensions -- 5 Reconstructions -- 6 Results -- 7 Discussion -- 8 Conclusions -- References -- Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization -- 1 Introduction -- 2 The Metaheuristics Studied -- 2.1 Basin Hopping -- 2.2 Differential Evolution -- 2.3 Particle Swarm Optimization -- 3 The Benchmarking Environment -- 4 Experimental Setup -- 5 Experimental Results -- 6 Conclusions -- References -- Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models -- 1 Introduction -- 2 Methodology -- 2.1 Structured Grammatical Evolution -- 2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems -- 3 Experimental Setup -- 3.1 Study Problems -- 3.2 Configuration of the Algorithms -- 4 Results -- 5 Conclusions -- References.
EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Framework Overview -- 4.1 Parameters -- 4.2 Datasets -- 4.3 Clustering with EvoCluster -- 4.4 Classification -- 4.5 Evaluation Measures -- 4.6 Results Management -- 5 Experiments and Visualizations -- 6 Conclusion and Future Works -- References -- Evolution of Acoustic Logic Gates in Granular Metamaterials -- 1 Introduction -- 2 Problem Statement -- 3 Simulation Setup -- 3.1 2D Granular Simulator -- 3.2 Optimization Method -- 4 Results and Discussion -- 4.1 Evolution of an Acoustic Band Gap -- 4.2 Evolving an AND Gate -- 4.3 Evolving an XOR Gate -- 5 Conclusion and Future Work -- References -- Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry -- 1 Introduction -- 2 The Problem -- 3 Proposed Approach -- 3.1 The Model -- 3.2 Data -- 3.3 Adversarial Optimization -- 3.4 Operator (EA1) -- 3.5 Public Administration (EA2) -- 4 Experimental Evaluation -- 4.1 Stochastic Optimization -- 4.2 Analysis -- 4.3 Real World Case -- 5 Conclusions and Future Work -- References -- The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization -- 1 Introduction -- 2 Background -- 2.1 Two-Body Problem -- 2.2 Maneuvers in Space -- 2.3 Lambert Problem -- 3 Asteroid Routing Problem -- 4 Optimization Algorithms -- 4.1 Sequential Least Squares Programming (SLSQP) -- 4.2 Greedy Nearest Neighbor Heuristic -- 4.3 Unbalanced Mallows Model (UMM) -- 4.4 Combinatorial Efficient Global Optimization (CEGO) -- 5 Experimental Study -- 5.1 Experimental Methodology -- 5.2 Results of the Black-Box Setting -- 5.3 Results of the Informed Setting -- 6 Conclusions -- References -- On the Difficulty of Evolving Permutation Codes -- 1 Introduction -- 2 Preliminaries. 3 Incremental Construction with EA -- 3.1 Evolving Subsets of Permutations -- 3.2 Iterative Approach -- 3.3 Fitness Functions -- 4 Experimental Evaluation -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 Moving Average -- 2.3 Population Diversity -- 2.4 Opposition-Based Learning -- 3 Proposed Approach -- 4 Experiments and Results -- 4.1 Experiments over 30 Dimensions -- 4.2 Experiments over 50 Dimensions -- 5 Conclusions and Future Work -- References -- Search-Based Third-Party Library Migration at the Method-Level -- 1 Introduction -- 2 Background and Motivation -- 2.1 Background -- 2.2 Motivating Example -- 3 Search-Based API Migration -- 3.1 Solution Representation -- 3.2 Calculating the Fitness Function -- 3.3 Genetic Algorithm Operators and Parameters -- 4 Experimental Evaluation -- 4.1 Dataset Used -- 4.2 Metrics Used -- 4.3 Results -- 4.4 Discussion and Limitations -- 5 Related Work -- 6 Conclusion -- References -- Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction -- 1 Introduction -- 2 Background -- 3 Methods -- 3.1 Individual Encoding -- 3.2 Optimization Algorithms -- 4 Experimental Setup -- 4.1 Benchmark Dataset -- 4.2 Back-Propagation Neural Networks (BPNNs) -- 4.3 Computational Setup and Data Preparation -- 5 Experimental Results -- 6 Conclusions -- References -- Explainable Landscape Analysis in Automated Algorithm Performance Prediction -- 1 Introduction -- 2 Related Work -- 3 Automated Algorithm Performance Prediction -- 4 Experimental Setup -- 4.1 Data -- 4.2 Regression Models and Their Hyper-parameters -- 4.3 Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References. Search Trajectories Networks of Multiobjective Evolutionary Algorithms -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Search Trajectory Networks -- 3.2 Multiobjective Optimisation Problems -- 4 STN Extension for the Multiobjective Domain -- 5 Experiments -- 5.1 Experimental Parameters -- 5.2 Metrics -- 5.3 Reproducibility -- 6 Results -- 7 Conclusion -- References -- EvoMCS: Optimising Energy and Throughput of Mission Critical Services -- 1 Introduction -- 2 Related Work -- 3 EvoMCS: Multi-objective Optimization -- 3.1 Scenario and Technologies -- 3.2 Evolutionary Algorithm -- 3.3 Heuristic for Fitness -- 3.4 Selection Strategy -- 3.5 Operators to Generate Descendants -- 4 Experimentation -- 4.1 Validation Scenarios -- 4.2 Configuration Parameters -- 4.3 Evaluation Metrics -- 4.4 Profiles Validation - Inputs from EvoMCS -- 5 Results -- 5.1 Operators for the EvoMCS in H1(E/T) -- 5.2 Optimal Configurations -- 5.3 Optimal Profiles in Scenarios with Dense-Environments -- 6 Conclusions -- References -- RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 L-SHADE -- 3 RWS-L-SHADE -- 4 Experimental Results -- 5 Conclusions -- References -- WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution -- 1 Introduction -- 2 Grammatical Evolution and Differential Evolution -- 3 Software Description -- 3.1 Modular Design -- 3.2 Parallel Execution -- 3.3 Persistence Layer -- 3.4 Implementation Technologies -- 4 WebGE Most Relevant Features -- 4.1 GUI for Experiments Management -- 4.2 Cross-fold Validation -- 4.3 Detailed Statistics -- 5 Use Case: Vladislavleva-4 -- 6 Conclusions -- References -- A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment. 1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Background and Related Work -- 2.1 Vibrational Spectroscopy -- 2.2 Partial Least Squares Regression -- 2.3 Spectral Pre-processing -- 2.4 PLSR for Nutrient Assessment -- 3 The Proposed Approach -- 3.1 Representations for the Two Populations for Co-evolution -- 3.2 Mapping of the Two Populations for Pairwise Evaluations -- 3.3 The Evaluation Method -- 4 Experiment Design -- 4.1 Datasets -- 4.2 Parameter Settings -- 5 Results and Discussions -- 5.1 Comparisons on the Training and Test Performance -- 5.2 Analyses on the Pre-processing Selection -- 5.3 Analyses on Feature Selection Results -- 6 Conclusions and Future Work -- References -- Evolutionary Computation in Edge, Fog, and Cloud Computing -- Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling -- 1 Introduction -- 2 Related Work -- 3 Job Scheduling Structures -- 4 Structure Optimisation -- 4.1 Brute Force Search Algorithm -- 4.2 Genetic Algorithm -- 4.3 Simulated Annealing Algorithm -- 5 Simulation Experiments and Results -- 5.1 Setup -- 5.2 Experiment 1: Search Algorithm Comparison -- 5.3 Experiment 2: Server Processing Power Dispersion Impact -- 5.4 Experiment 3: Task Size Dispersion Impact -- 5.5 Experiment 4: Job Complexity Impact -- 6 Conclusion -- References -- Optimising Communication Overhead in Federated Learning Using NSGA-II -- 1 Introduction -- 2 Fundamental Concepts -- 2.1 Federated Learning -- 2.2 Communication Overhead in Distributed Deep Learning -- 3 Proposed Approach -- 3.1 The Proposed FL-COP Modelling and Formulation -- 3.2 The Communication-Overhead Reduction Routine -- 4 Experimental Study and Analysis -- 4.1 Problem Benchmarks and Experimental Settings -- 4.2 Experimental Results and Discussion -- 5 Conclusions and Perspectives -- References -- Evolutionary Machine Learning. Evolving Data Augmentation Strategies. |
| Record Nr. | UNISA-996472070103316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Applications of evolutionary computation : 25th European conference, EvoApplications 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings / / edited by Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, and Kehinde Oluwatoyin Babaagba
| Applications of evolutionary computation : 25th European conference, EvoApplications 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings / / edited by Juan Luis Jiménez Laredo, J. Ignacio Hidalgo, and Kehinde Oluwatoyin Babaagba |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (759 pages) |
| Disciplina | 006.3823 |
| Collana | Lecture Notes in Computer Science |
| Soggetto topico | Evolutionary computation |
| ISBN | 3-031-02462-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto |
Intro -- Preface -- Organization -- Contents -- Applications of Evolutionary Computation -- An Enhanced Opposition-Based Evolutionary Feature Selection Approach -- 1 Introduction -- 2 Moth Flame Optimization -- 2.1 Binary Moth Flame Optimization -- 2.2 Binary Moth Flame Optimization for Feature Selection -- 3 The Proposed Approach -- 3.1 Initialization Using Opposition-Based Method -- 3.2 Retiring Flame -- 4 Experimental Setup and Results -- 5 Conclusions -- References -- A Methodology for Determining Ion Channels from Membrane Potential Neuronal Recordings -- 1 Introduction -- 2 Conductance-Based Model Description -- 3 Defining a Benchmark with Known Types of Ion Channels -- 4 Methodology and Experimental Setup -- 5 Experimental Results -- 6 Conclusions -- A Mathematical Description of the Models -- B Experimental Setup and Parameter Ranges -- References -- Swarm Optimised Few-View Binary Tomography -- 1 Introduction -- 2 Binary Tomographic Reconstruction -- 3 Swarm Optimisation -- 4 Constrained Search in High Dimensions -- 5 Reconstructions -- 6 Results -- 7 Discussion -- 8 Conclusions -- References -- Comparing Basin Hopping with Differential Evolution and Particle Swarm Optimization -- 1 Introduction -- 2 The Metaheuristics Studied -- 2.1 Basin Hopping -- 2.2 Differential Evolution -- 2.3 Particle Swarm Optimization -- 3 The Benchmarking Environment -- 4 Experimental Setup -- 5 Experimental Results -- 6 Conclusions -- References -- Combining the Properties of Random Forest with Grammatical Evolution to Construct Ensemble Models -- 1 Introduction -- 2 Methodology -- 2.1 Structured Grammatical Evolution -- 2.2 Random Structured Grammatical Evolution for Symbolic Regression Problems -- 3 Experimental Setup -- 3.1 Study Problems -- 3.2 Configuration of the Algorithms -- 4 Results -- 5 Conclusions -- References.
EvoCC: An Open-Source Classification-Based Nature-Inspired Optimization Clustering Framework in Python -- 1 Introduction -- 2 Related Works -- 3 Methodology -- 4 Framework Overview -- 4.1 Parameters -- 4.2 Datasets -- 4.3 Clustering with EvoCluster -- 4.4 Classification -- 4.5 Evaluation Measures -- 4.6 Results Management -- 5 Experiments and Visualizations -- 6 Conclusion and Future Works -- References -- Evolution of Acoustic Logic Gates in Granular Metamaterials -- 1 Introduction -- 2 Problem Statement -- 3 Simulation Setup -- 3.1 2D Granular Simulator -- 3.2 Optimization Method -- 4 Results and Discussion -- 4.1 Evolution of an Acoustic Band Gap -- 4.2 Evolving an AND Gate -- 4.3 Evolving an XOR Gate -- 5 Conclusion and Future Work -- References -- Public-Private Partnership: Evolutionary Algorithms as a Solution to Information Asymmetry -- 1 Introduction -- 2 The Problem -- 3 Proposed Approach -- 3.1 The Model -- 3.2 Data -- 3.3 Adversarial Optimization -- 3.4 Operator (EA1) -- 3.5 Public Administration (EA2) -- 4 Experimental Evaluation -- 4.1 Stochastic Optimization -- 4.2 Analysis -- 4.3 Real World Case -- 5 Conclusions and Future Work -- References -- The Asteroid Routing Problem: A Benchmark for Expensive Black-Box Permutation Optimization -- 1 Introduction -- 2 Background -- 2.1 Two-Body Problem -- 2.2 Maneuvers in Space -- 2.3 Lambert Problem -- 3 Asteroid Routing Problem -- 4 Optimization Algorithms -- 4.1 Sequential Least Squares Programming (SLSQP) -- 4.2 Greedy Nearest Neighbor Heuristic -- 4.3 Unbalanced Mallows Model (UMM) -- 4.4 Combinatorial Efficient Global Optimization (CEGO) -- 5 Experimental Study -- 5.1 Experimental Methodology -- 5.2 Results of the Black-Box Setting -- 5.3 Results of the Informed Setting -- 6 Conclusions -- References -- On the Difficulty of Evolving Permutation Codes -- 1 Introduction -- 2 Preliminaries. 3 Incremental Construction with EA -- 3.1 Evolving Subsets of Permutations -- 3.2 Iterative Approach -- 3.3 Fitness Functions -- 4 Experimental Evaluation -- 4.1 Experimental Settings -- 4.2 Results -- 5 Conclusions and Future Work -- References -- Improving the Convergence and Diversity in Differential Evolution Through a Stock Market Criterion -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 Moving Average -- 2.3 Population Diversity -- 2.4 Opposition-Based Learning -- 3 Proposed Approach -- 4 Experiments and Results -- 4.1 Experiments over 30 Dimensions -- 4.2 Experiments over 50 Dimensions -- 5 Conclusions and Future Work -- References -- Search-Based Third-Party Library Migration at the Method-Level -- 1 Introduction -- 2 Background and Motivation -- 2.1 Background -- 2.2 Motivating Example -- 3 Search-Based API Migration -- 3.1 Solution Representation -- 3.2 Calculating the Fitness Function -- 3.3 Genetic Algorithm Operators and Parameters -- 4 Experimental Evaluation -- 4.1 Dataset Used -- 4.2 Metrics Used -- 4.3 Results -- 4.4 Discussion and Limitations -- 5 Related Work -- 6 Conclusion -- References -- Multi-objective Optimization of Extreme Learning Machine for Remaining Useful Life Prediction -- 1 Introduction -- 2 Background -- 3 Methods -- 3.1 Individual Encoding -- 3.2 Optimization Algorithms -- 4 Experimental Setup -- 4.1 Benchmark Dataset -- 4.2 Back-Propagation Neural Networks (BPNNs) -- 4.3 Computational Setup and Data Preparation -- 5 Experimental Results -- 6 Conclusions -- References -- Explainable Landscape Analysis in Automated Algorithm Performance Prediction -- 1 Introduction -- 2 Related Work -- 3 Automated Algorithm Performance Prediction -- 4 Experimental Setup -- 4.1 Data -- 4.2 Regression Models and Their Hyper-parameters -- 4.3 Evaluation -- 5 Results and Discussion -- 6 Conclusion -- References. Search Trajectories Networks of Multiobjective Evolutionary Algorithms -- 1 Introduction -- 2 Related Work -- 3 Preliminaries -- 3.1 Search Trajectory Networks -- 3.2 Multiobjective Optimisation Problems -- 4 STN Extension for the Multiobjective Domain -- 5 Experiments -- 5.1 Experimental Parameters -- 5.2 Metrics -- 5.3 Reproducibility -- 6 Results -- 7 Conclusion -- References -- EvoMCS: Optimising Energy and Throughput of Mission Critical Services -- 1 Introduction -- 2 Related Work -- 3 EvoMCS: Multi-objective Optimization -- 3.1 Scenario and Technologies -- 3.2 Evolutionary Algorithm -- 3.3 Heuristic for Fitness -- 3.4 Selection Strategy -- 3.5 Operators to Generate Descendants -- 4 Experimentation -- 4.1 Validation Scenarios -- 4.2 Configuration Parameters -- 4.3 Evaluation Metrics -- 4.4 Profiles Validation - Inputs from EvoMCS -- 5 Results -- 5.1 Operators for the EvoMCS in H1(E/T) -- 5.2 Optimal Configurations -- 5.3 Optimal Profiles in Scenarios with Dense-Environments -- 6 Conclusions -- References -- RWS-L-SHADE: An Effective L-SHADE Algorithm Incorporation Roulette Wheel Selection Strategy for Numerical Optimisation -- 1 Introduction -- 2 Background -- 2.1 Differential Evolution -- 2.2 L-SHADE -- 3 RWS-L-SHADE -- 4 Experimental Results -- 5 Conclusions -- References -- WebGE: An Open-Source Tool for Symbolic Regression Using Grammatical Evolution -- 1 Introduction -- 2 Grammatical Evolution and Differential Evolution -- 3 Software Description -- 3.1 Modular Design -- 3.2 Parallel Execution -- 3.3 Persistence Layer -- 3.4 Implementation Technologies -- 4 WebGE Most Relevant Features -- 4.1 GUI for Experiments Management -- 4.2 Cross-fold Validation -- 4.3 Detailed Statistics -- 5 Use Case: Vladislavleva-4 -- 6 Conclusions -- References -- A New Genetic Algorithm for Automated Spectral Pre-processing in Nutrient Assessment. 1 Introduction -- 1.1 Goals -- 1.2 Organisation -- 2 Background and Related Work -- 2.1 Vibrational Spectroscopy -- 2.2 Partial Least Squares Regression -- 2.3 Spectral Pre-processing -- 2.4 PLSR for Nutrient Assessment -- 3 The Proposed Approach -- 3.1 Representations for the Two Populations for Co-evolution -- 3.2 Mapping of the Two Populations for Pairwise Evaluations -- 3.3 The Evaluation Method -- 4 Experiment Design -- 4.1 Datasets -- 4.2 Parameter Settings -- 5 Results and Discussions -- 5.1 Comparisons on the Training and Test Performance -- 5.2 Analyses on the Pre-processing Selection -- 5.3 Analyses on Feature Selection Results -- 6 Conclusions and Future Work -- References -- Evolutionary Computation in Edge, Fog, and Cloud Computing -- Dynamic Hierarchical Structure Optimisation for Cloud Computing Job Scheduling -- 1 Introduction -- 2 Related Work -- 3 Job Scheduling Structures -- 4 Structure Optimisation -- 4.1 Brute Force Search Algorithm -- 4.2 Genetic Algorithm -- 4.3 Simulated Annealing Algorithm -- 5 Simulation Experiments and Results -- 5.1 Setup -- 5.2 Experiment 1: Search Algorithm Comparison -- 5.3 Experiment 2: Server Processing Power Dispersion Impact -- 5.4 Experiment 3: Task Size Dispersion Impact -- 5.5 Experiment 4: Job Complexity Impact -- 6 Conclusion -- References -- Optimising Communication Overhead in Federated Learning Using NSGA-II -- 1 Introduction -- 2 Fundamental Concepts -- 2.1 Federated Learning -- 2.2 Communication Overhead in Distributed Deep Learning -- 3 Proposed Approach -- 3.1 The Proposed FL-COP Modelling and Formulation -- 3.2 The Communication-Overhead Reduction Routine -- 4 Experimental Study and Analysis -- 4.1 Problem Benchmarks and Experimental Settings -- 4.2 Experimental Results and Discussion -- 5 Conclusions and Perspectives -- References -- Evolutionary Machine Learning. Evolving Data Augmentation Strategies. |
| Record Nr. | UNINA-9910561298603321 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
