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Learning and Intelligent Optimization

9th International Conference, LION 9, Lille, France, January 12-15, 2015. Revised Selected Papers

  • Conference proceedings
  • © 2015

Overview

Editors:
  1. Clarisse Dhaenens

    1. Lille University, Villeneuve d'Ascq, France

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  2. Laetitia Jourdan

    1. Lille University, Villeneuve d'Ascq, France

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  3. Marie-Eléonore Marmion

    1. Lille University, Villeneuve d'Ascq, France

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 8994)

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

Included in the following conference series:

Conference proceedings info: LION 2015.

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About this book

This book constitutes the thoroughly refereed post-conference proceedings of the 9th International Conference on Learning and Optimization, LION 9, which was held in Lille, France, in January 2015.

The 31 contributions presented were carefully reviewed and selected for inclusion in this book. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to algorithm selection and configuration, learning, fitness landscape, applications, dynamic optimization, multi-objective, max-clique problems, bayesian optimization and global optimization, data mining and - in a special session - also on dynamic optimization.

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Table of contents (31 papers)

  1. Front Matter

    Pages I-XI
    Download chapter PDF

  2. From Sequential Algorithm Selection to Parallel Portfolio Selection

    • M. Lindauer, H. Hoos, F. Hutter
    Pages 1-16

  3. An Algorithm Selection Benchmark of the Container Pre-marshalling Problem

    • Kevin Tierney, Yuri Malitsky
    Pages 17-22

  4. ADVISER: A Web-Based Algorithm Portfolio Deviser

    • Mustafa Mısır, Stephanus Daniel Handoko, Hoong Chuin Lau
    Pages 23-28

  5. Identifying Best Hyperparameters for Deep Architectures Using Random Forests

    • Zhen-Zhen Li, Zhuo-Yao Zhong, Lian-Wen Jin
    Pages 29-42

  6. Programming by Optimisation Meets Parameterised Algorithmics: A Case Study for Cluster Editing

    • Sepp Hartung, Holger H. Hoos
    Pages 43-58

  7. OSCAR: Online Selection of Algorithm Portfolios with Case Study on Memetic Algorithms

    • Mustafa Mısır, Stephanus Daniel Handoko, Hoong Chuin Lau
    Pages 59-73

  8. Learning a Hidden Markov Model-Based Hyper-heuristic

    • Willem Van Onsem, Bart Demoen, Patrick De Causmaecker
    Pages 74-88

  9. Comparison of Parameter Control Mechanisms in Multi-objective Differential Evolution

    • Martin Drozdik, Hernan Aguirre, Youhei Akimoto, Kiyoshi Tanaka
    Pages 89-103

  10. Genetic Programming, Logic Design and Case-Based Reasoning for Obstacle Avoidance

    • Andy Keane
    Pages 104-118

  11. Minimizing Total Tardiness on Identical Parallel Machines Using VNS with Learning Memory

    • Eduardo Lalla-Ruiz, Stefan Voß
    Pages 119-124

  12. Dynamic Service Selection with Optimal Stopping and ‘Trivial Choice’

    • Oliver Skroch
    Pages 125-130

  13. A Comparative Study on Self-Adaptive Differential Evolution Algorithms for Test Functions and a Real-World Problem

    • Shota Eguchi, Yuki Matsugano, Hirokazu Sakaguchi, Satoshi Ono, Hisato Fukuda, Ryo Furukawa et al.
    Pages 131-136

  14. Empirical Analysis of Operators for Permutation Based Problems

    • Pierre Desport, Matthieu Basseur, Adrien Goëffon, Frédéric Lardeux, Frédéric Saubion
    Pages 137-150

  15. Fitness Landscape of the Factoradic Representation on the Permutation Flowshop Scheduling Problem

    • Marie-Eléonore Marmion, Olivier Regnier-Coudert
    Pages 151-164

  16. Exploring Non-neutral Landscapes with Neutrality-Based Local Search

    • Matthieu Basseur, Adrien Goëffon, Hugo Traverson
    Pages 165-169

  17. A Selector Operator-Based Adaptive Large Neighborhood Search for the Covering Tour Problem

    • Leticia Vargas, Nicolas Jozefowiez, Sandra Ulrich Ngueveu
    Pages 170-185

  18. Metaheuristics for the Two-Dimensional Container Pre-Marshalling Problem

    • Alan Tus, Andrea Rendl, Günther R. Raidl
    Pages 186-201

  19. Improving the State of the Art in Inexact TSP Solving Using Per-Instance Algorithm Selection

    • Lars Kotthoff, Pascal Kerschke, Holger Hoos, Heike Trautmann
    Pages 202-217

  20. A Biased Random-Key Genetic Algorithm for the Multiple Knapsack Assignment Problem

    • Eduardo Lalla-Ruiz, Stefan Voß
    Pages 218-222
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Other volumes

  1. Learning and Intelligent Optimization

Editors and Affiliations

  • Lille University, Villeneuve d'Ascq, France

    Clarisse Dhaenens, Laetitia Jourdan, Marie-Eléonore Marmion

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