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

• Record Nr.: UNISA996472070103316
• Pubbl/distr/stampa: Cham, Switzerland : , : Springer, , [2022]; ©2022
• ISBN: 3-031-02462-1
• Descrizione fisica: 1 online resource (759 pages)
• Collana: Lecture Notes in Computer Science ; ; v.13224
• Disciplina: 006.3823
• Soggetti: Evolutionary computation
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Nota di bibliografia: Includes bibliographical references and index.
• 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.