LEADER 11154nam 2200517 450 001 996472070103316 005 20231110233354.0 010 $a3-031-02462-1 035 $a(MiAaPQ)EBC6953668 035 $a(Au-PeEL)EBL6953668 035 $a(CKB)21513297600041 035 $a(PPN)262167522 035 $a(EXLCZ)9921513297600041 100 $a20221118d2022 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aApplications of evolutionary computation $e25th European conference, EvoApplications 2022, held as part of EvoStar 2022, Madrid, Spain, April 20-22, 2022, proceedings /$fedited by Juan Luis Jime?nez Laredo, J. Ignacio Hidalgo, and Kehinde Oluwatoyin Babaagba 210 1$aCham, Switzerland :$cSpringer,$d[2022] 210 4$d©2022 215 $a1 online resource (759 pages) 225 1 $aLecture Notes in Computer Science ;$vv.13224 311 08$aPrint version: Jiménez Laredo, Juan Luis Applications of Evolutionary Computation Cham : Springer International Publishing AG,c2022 9783031024610 320 $aIncludes bibliographical references and index. 327 $aIntro -- 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. 327 $aEvoCC: 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. 327 $a3 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. 327 $aSearch 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. 327 $a1 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. 327 $aEvolving Data Augmentation Strategies. 410 0$aLecture Notes in Computer Science 606 $aEvolutionary computation 615 0$aEvolutionary computation. 676 $a006.3823 702 $aBabaagba$b Kehinde Oluwatoyin 702 $aHidalgo Pe?rez$b Jose? Ignacio 702 $aLaredo$b Juan Luis Jime?nez 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996472070103316 996 $aApplications of Evolutionary Computation$92834163 997 $aUNISA LEADER 03172oam 2200637I 450 001 9910967098303321 005 20251116195748.0 010 $a1-136-49669-6 010 $a1-136-49676-9 010 $a1-315-01608-7 024 7 $a10.4324/9781315016085 035 $a(CKB)3710000000056575 035 $a(SSID)ssj0001155224 035 $a(PQKBManifestationID)11746925 035 $a(PQKBTitleCode)TC0001155224 035 $a(PQKBWorkID)11179479 035 $a(PQKB)10556493 035 $a(MiAaPQ)EBC1542748 035 $a(Au-PeEL)EBL1542748 035 $a(CaPaEBR)ebr10800336 035 $a(CaONFJC)MIL762323 035 $a(OCoLC)863823550 035 $a(OCoLC)863157939 035 $a(FINmELB)ELB140479 035 $a(EXLCZ)993710000000056575 100 $a20180706d2004 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt 182 $cc 183 $acr 200 00$aCritical inquiry and problem-solving in physical education /$fedited by Jan Wright, Doune MacDonald, and Lisette Burrows 205 $a1st ed. 210 1$aLondon ;$aNew York :$cRoutledge,$d2004. 215 $a1 online resource (227 pages) $cillustrations 300 $aBibliographic Level Mode of Issuance: Monograph 311 08$a0-415-29164-X 311 08$a0-415-29163-1 320 $aIncludes bibliographical references and index. 327 $apart I. Locating critical inquiry and problem-solving in physical education -- part II. Critical inquiry and problem-solving in the middle years of schooling -- part III. Critical inquiry and problem-solving in the senior years of schooling -- part IV. The challenges of critical inquiry in physical education. 330 $aCritical inquiry, critical thinking and problem-solving are key concepts in contemporary physical education. But how do physical educators actually do critical inquiry and critical thinking? Critical Inquiry and Problem-Solving in Physical Education explains the principles and assumptions underpinning these concepts and provides detailed examples of how they can be used in the teaching of physical education for different age groups and in a range of different contexts. Topics covered include: sport education and critical thinking dance as critical inquiry media analysis understanding cultural perspectives student-led research and curriculum reflective coaching practice. The authors are teachers, teacher educators, policymakers and academics. Each shares a commitment to the notion that school students can do more than learn to move in physical education classes. 606 $aPhysical education and training$xStudy and teaching 606 $aProblem solving 615 0$aPhysical education and training$xStudy and teaching. 615 0$aProblem solving. 676 $a613.7/07 701 $aBurrows$b Lisette$f1963-$01880454 701 $aMacDonald$b Doune$f1959-$01880455 701 $aWright$b Jan$f1948-$01351687 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910967098303321 996 $aCritical inquiry and problem-solving in physical education$94494438 997 $aUNINA