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Learning and Intelligent Optimization

15th International Conference, LION 15, Athens, Greece, June 20–25, 2021, Revised Selected Papers

  • Conference proceedings
  • © 2021

Overview

Editors:
  1. Dimitris E. Simos

    1. SBA Research, Vienna, Austria

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  2. Panos M. Pardalos

    1. University of Florida, Gainesville, USA

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  3. Ilias S. Kotsireas

    1. Wilfrid Laurier University, Waterloo, Canada

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 12931)

Part of the book sub series: Theoretical Computer Science and General Issues (LNTCS)

Included in the following conference series:

Conference proceedings info: LION 2021.

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Table of contents (30 papers)

  1. Front Matter

    Pages i-xiii
    Download chapter PDF

  2. An Optimization for Convolutional Network Layers Using the Viola-Jones Framework and Ternary Weight Networks

    • Rhys Agombar, Christian Bauckhage, Max Luebbering, Rafet Sifa
    Pages 1-6

  3. Learning to Optimize Black-Box Functions with Extreme Limits on the Number of Function Evaluations

    • Carlos Ansótegui, Meinolf Sellmann, Tapan Shah, Kevin Tierney
    Pages 7-24

  4. Graph Diffusion & PCA Framework for Semi-supervised Learning

    • Konstantin Avrachenkov, Aurélie Boisbunon, Mikhail Kamalov
    Pages 25-39

  5. Exact Counting and Sampling of Optima for the Knapsack Problem

    • Jakob Bossek, Aneta Neumann, Frank Neumann
    Pages 40-54

  6. Modeling of Crisis Periods in Stock Markets

    • Apostolos Chalkis, Emmanouil Christoforou, Theodore Dalamagas, Ioannis Z. Emiris
    Pages 55-65

  7. Feature Selection in Single-Cell RNA-seq Data via a Genetic Algorithm

    • Konstantinos I. Chatzilygeroudis, Aristidis G. Vrahatis, Sotiris K. Tasoulis, Michael N. Vrahatis
    Pages 66-79

  8. Towards Complex Scenario Instances for the Urban Transit Routing Problem

    • Roberto Díaz Urra, Carlos Castro, Nicolás Gálvez Ramírez
    Pages 80-97

  9. Spirometry-Based Airways Disease Simulation and Recognition Using Machine Learning Approaches

    • Riccardo Di Dio, André Galligo, Angelos Mantzaflaris, Benjamin Mauroy
    Pages 98-112

  10. Long-Term Hypertension Risk Prediction with ML Techniques in ELSA Database

    • Elias Dritsas, Nikos Fazakis, Otilia Kocsis, Nikos Fakotakis, Konstantinos Moustakas
    Pages 113-120

  11. An Efficient Heuristic for Passenger Bus VRP with Preferences and Tradeoffs

    • Suhendry Effendy, Bao Chau Ngo, Roland H. C. Yap
    Pages 121-127

  12. Algorithm for Predicting the Quality of the Product Based on Technological Pyramids in Graphs

    • Damir N. Gainanov, Dmitriy A. Berenov, Varvara A. Rasskazova
    Pages 128-141

  13. Set Team Orienteering Problem with Time Windows

    • Aldy Gunawan, Vincent F. Yu, Andro Nicus Sutanto, Panca Jodiawan
    Pages 142-149

  14. Reparameterization of Computational Chemistry Force Fields Using GloMPO (Globally Managed Parallel Optimization)

    • Michael Freitas Gustavo, Toon Verstraelen
    Pages 150-156

  15. Towards Structural Hyperparameter Search in Kernel Minimum Enclosing Balls

    • Hanna Kondratiuk, Rafet Sifa
    Pages 157-166

  16. Using Past Experience for Configuration of Gaussian Processes in Black-Box Optimization

    • Jan Koza, Jiří Tumpach, Zbyněk Pitra, Martin Holeňa
    Pages 167-182

  17. Travel Demand Estimation in a Multi-subnet Urban Road Network

    • Alexander Krylatov, Anastasiya Raevskaya
    Pages 183-197

  18. The Shortest Simple Path Problem with a Fixed Number of Must-Pass Nodes: A Problem-Specific Branch-and-Bound Algorithm

    • Andrei Kudriavtsev, Daniel Khachay, Yuri Ogorodnikov, Jie Ren, Sheng Cheng Shao, Dong Zhang et al.
    Pages 198-210

  19. Medical Staff Scheduling Problem in Chinese Mobile Cabin Hospitals During Covid-19 Outbreak

    • Shaowen Lan, Wenjuan Fan, Kaining Shao, Shanlin Yang, Panos M. Pardalos
    Pages 211-218

  20. Performance Evaluation of Adversarial Attacks on Whole-Graph Embedding Models

    • Mario Manzo, Maurizio Giordano, Lucia Maddalena, Mario R. Guarracino
    Pages 219-236
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  1. Learning and Intelligent Optimization

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About this book

This book constitutes the refereed post-conference proceedings on Learning and Intelligent Optimization, LION 15, held in Athens, Greece, in June 2021.

The 30 full papers presented have been carefully reviewed and selected from 35 submissions. LION deals with designing and engineering ways of "learning" about the performance of different techniques, and ways of using past experience about the algorithm behavior to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained online or offline can improve the algorithm design process and simplify the applications of high-performance optimization methods. Combinations of different algorithms can further improve the robustness and performance of the individual components.

Editors and Affiliations

  • SBA Research, Vienna, Austria

    Dimitris E. Simos

  • University of Florida, Gainesville, USA

    Panos M. Pardalos

  • Wilfrid Laurier University, Waterloo, Canada

    Ilias S. Kotsireas

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