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Parallel Problem Solving from Nature – PPSN XVII : 17th International Conference, PPSN 2022, Dortmund, Germany, September 10–14, 2022, Proceedings, Part II / edited by Günter Rudolph, Anna V. Kononova, Hernán Aguirre, Pascal Kerschke, Gabriela Ochoa, Tea Tušar
(Lecture Notes in Computer Science. ISSN:16113349 ; 13399)
| データ種別 | 電子ブック |
|---|---|
| 版 | 1st ed. 2022. |
| 出版者 | (Cham : Springer International Publishing : Imprint: Springer) |
| 出版年 | 2022 |
| 大きさ | XXIII, 629 p. 134 illus., 121 illus. in color : online resource |
| 著者標目 | Rudolph, Günter editor Kononova, Anna V editor Aguirre, Hernán editor Kerschke, Pascal editor Ochoa, Gabriela editor Tušar, Tea editor SpringerLink (Online service) |
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| 一般注記 | Automated Algorithm Selection in Single-Objective Continuous Optimization: A Comparative Study of Deep Learning and Landscape Analysis Methods -- Improving Nevergrad's Algorithm Selection Wizard NGOpt through Automated Algorithm Configuration -- Non-Elitist Selection Can Improve the Performance of Irace -- Per-run Algorithm Selection with Warm-starting using Trajectory-based Features -- Efficient Approximation of Expected Hypervolume Improvement using Gauss-Hermite Quadrature -- A Systematic Approach to Analyze the Computational Cost of Robustness in Model-Assisted Robust Optimization -- Adaptive Function Value Warping for Surrogate Model Assisted Evolutionary Optimization -- Finding Knees in Bayesian Multi-Objective Optimization -- High Dimensional Bayesian Optimization with Kernel Principal Component Analysis -- Single Interaction Multi-Objective Bayesian Optimization -- Surrogate-assisted LSHADE algorithm utilizing Recursive Least Squares filter -- Towards Efficient Multiobjective Hyperparameter Optimization: A Multiobjective Multi-Fidelity Bayesian Optimization and Hyperband Algorithm -- A Continuous Optimisation Benchmark Suite from Neural Network Regression -- BBE: Basin-Based Evaluation of Multimodal Multi-Objective Optimization Problems -- Evolutionary Approaches to Improving the Layouts of Instance-Spaces -- A novelty-search approach to filling an instance-space with diverse and discriminatory instances for the knapsack problem -- Co-Evolutionary Diversity Optimisation for the Traveling Thief Problem -- Computing High-Quality Solutions for the Patient Admission Scheduling Problem using Evolutionary Diversity Optimisation -- Cooperative Multi-Agent Search on Endogenously-Changing Fitness Landscapes -- Evolutionary Algorithm for Vehicle Routing with Diversity Oscillation Mechanism -- Evolutionary Algorithms for Limiting the Effect of Uncertainty for the Knapsack Problem with Stochastic Profits -- Self-adaptation via Multi-objectivisation: An Empirical Study -- The Combined Critical Node and Edge Detection Problem. An Evolutionary Approach -- Attention-Based Genetic Algorithm for Adversarial Attack in Natural Language Processing -- Deep Reinforcement Learning with Two-Stage Training Strategy for Practical Electric Vehicle Routing Problem with Time Windows -- Evolving Through the Looking Glass: Learning Improved Search Spaces with Variational Autoencoders -- Generalization and Computation for Policy Classes of Generative Adversarial Imitation Learning -- Generative Models over Neural Controllers for Transfer Learning -- HVC-Net: Deep Learning based Hypervolume Contribution Approximation -- Multi-objective Evolutionary Ensemble Pruning Guided by Margin Distribution -- Revisiting Attention-based Graph Neural Networks for Graph Classification -- Robust Neural Network Pruning by Cooperative Coevolution -- SemiGraphFL: Semi-Supervised Graph Federated Learning -- Evolutionary Design of Reduced Precision Preprocessor for Levodopa-Induced Dyskinesia Classifier -- In-Materio Extreme Learning Machines -- On the impact of the duration of evaluation episodes on the evolution of adaptive robots -- Analysing the Fitness Landscape Rotation for Combinatorial Optimisation -- Analysis of Search Landscape Samplers for Solver Performance Prediction on a University Timetabling Problem -- Fractal Dimension and Perturbation Strength: A Local Optima Networks View -- HPO X ELA: Investigating Hyperparameter Optimization Landscapes by Means of Exploratory Landscape Analysis -- Increasing the Diversity of Benchmark Function Sets through Affine Recombination -- Neural Architecture Search: A Visual Analysis. - Digging into Semantics: Where do search-based software repair methods search? -- Gene-pool Optimal Mixing in Cartesian Genetic Programming -- Genetic programming for combining directional changes indicators in international stock markets -- Importance-Aware Genetic Programming for Automated Scheduling Heuristics Learning in Dynamic Flexible Job Shop Scheduling -- Towards Discrete Phenotypic Recombination in Cartesian Genetic Programming -- A general architecture for generating interactive decomposition-based MOEAs -- An Exact Inverted Generational Distance for Continuous Pareto Front -- Direction Vector Selection for R2-based Hypervolume Contribution Approximation -- Do We Really Need to Use Constraint Violation in Constrained Evolutionary Multi-Objective Optimization? -- Dynamic Multi-modal Multi-objective Optimization: A Preliminary Study -- Fair Feature Selection with a Lexicographic Multi-Objective Genetic Algorithm -- Greedy Decremental Quick Hypervolume Subset Selection Algorithms -- Hybridizing Hypervolume-based Evolutionary Algorithms and Gradient Descent by Dynamic Resource Allocation -- Identifying Stochastically Non-dominated Solutions Using Evolutionary Computation -- Large-scale multi-objective influence maximisation with network downscaling -- Multi-Objective Evolutionary Algorithm Based on the Linear Assignment Problem and the Hypervolume Approximation using Polar Coordinates (MOEA-LAPCO) -- New Solution Creation Operator in MOEA/D for Faster Convergence -- Obtaining Smoothly Navigable Approximation Sets in Bi-Objective Multi-Modal Optimization -- T-DominO: Exploring Multiple Criteria with Quality-Diversity and the Tournament Dominance Objective -- Recombination Weight based Selection in the DTS-CMA-ES -- The (1+1)-ES Reliably Overcomes Saddle Points -- Collective Learning of Low-Memory Matrix Adaptation for Large-Scale Black-Box Optimization -- Evolutionary Time-Use Optimization for Improving Children's Health Outcomes -- Iterated Local Search for the eBuses Charging Location Problem -- Multi-view clustering of heterogeneous health data: Application to systemic sclerosis -- Specification-Driven Evolution of Floor Plan Design -- Surrogate-assisted Multi-objective Optimization for Compiler Optimization Sequence Selection -- A First Runtime Analysis of the NSGA-II on a Multimodal Problem -- Analysis of Quality Diversity Algorithms for the Knapsack Problem -- Better Running Time of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) by Using Stochastic Tournament Selection -- Escaping Local Optima With Local Search: A Theory-Driven Discussion -- Evolutionary Algorithms for Cardinality-Constrained Ising Models -- General Univariate Estimation-of-Distribution Algorithms -- Population Diversity Leads to Short Running Times of Lexicase Selection -- Progress Rate Analysis of Evolution Strategies on the Rastrigin Function: First Results -- Running Time Analysis of the (1+1)-EA using Surrogate Models on OneMax and LeadingOnes -- Runtime Analysis of Simple Evolutionary Algorithms for the Chance-constrained Makespan Scheduling Problem -- Runtime Analysis of the (1+1) EA on Weighted Sums of Transformed Linear Functions -- Runtime Analysis of Unbalanced Block-Parallel Evolutionary Algorithms -- Self-adjusting Population Sizes for the (1, λ)-EA on Monotone Functions -- Theoretical Study of Optimizing Rugged Landscapes with the cGA -- Towards Fixed-Target Black-Box Complexity Analysis -- Two-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously Can Be Hard This two-volume set LNCS 13398 and LNCS 13399 constitutes the refereed proceedings of the 17th International Conference on Parallel Problem Solving from Nature, PPSN 2022, held in Dortmund, Germany, in September 2022. The 87 revised full papers were carefully reviewed and selected from numerous submissions. The conference presents a study of computing methods derived from natural models. Amorphous Computing, Artificial Life, Artificial Ant Systems, Artificial Immune Systems, Artificial Neural Networks, Cellular Automata, Evolutionary Computation, Swarm Computing, Self-Organizing Systems, Chemical Computation, Molecular Computation, Quantum Computation, Machine Learning, and Artificial Intelligence approaches using Natural Computing methods are just some of the topics covered in this field HTTP:URL=https://doi.org/10.1007/978-3-031-14721-0 |
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| 件 名 | LCSH:Machine learning LCSH:Software engineering LCSH:Application software LCSH:Computer engineering LCSH:Computer networks LCSH:Computers LCSH:Computer systems FREE:Machine Learning FREE:Software Engineering FREE:Computer and Information Systems Applications FREE:Computer Engineering and Networks FREE:Computing Milieux FREE:Computer System Implementation |
| 分 類 | LCC:Q325.5-.7 DC23:006.31 |
| 書誌ID | EB00001872 |
| ISBN | 9783031147210 |
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