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Learning and Intelligent Optimization

Second International Conference, LION 2007 II, Trento, Italy, December 8-12, 2007. Selected Papers

  • Conference proceedings
  • © 2008

Overview

Editors:
  1. Vittorio Maniezzo

    1. Dept. Computer Science, University of Bologna, Bologna, Italy

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    You can also search for this editor in PubMed   Google Scholar

  2. Roberto Battiti

    1. Università degli Studi di Trento, Trento, Italy

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    You can also search for this editor in PubMed   Google Scholar

  3. Jean-Paul Watson

    1. Discrete Math and Complex Systems Department, Sandia National Laboratories, Albuquerque, USA

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    You can also search for this editor in PubMed   Google Scholar

Part of the book series: Lecture Notes in Computer Science (LNCS, volume 5313)

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

Included in the following conference series:

Conference proceedings info: LION 2007.

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Table of contents (18 papers)

  1. Front Matter

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  2. Nested Partitioning for the Minimum Energy Broadcast Problem

    • Sameh Al-Shihabi, Peter Merz, Steffen Wolf
    Pages 1-11

  3. An Adaptive Memory-Based Approach Based on Partial Enumeration

    • Enrico Bartolini, Vittorio Maniezzo, Aristide Mingozzi
    Pages 12-24

  4. Learning While Optimizing an Unknown Fitness Surface

    • Roberto Battiti, Mauro Brunato, Paolo Campigotto
    Pages 25-40

  5. On Effectively Finding Maximal Quasi-cliques in Graphs

    • Mauro Brunato, Holger H. Hoos, Roberto Battiti
    Pages 41-55

  6. Improving the Exploration Strategy in Bandit Algorithms

    • Olivier Caelen, Gianluca Bontempi
    Pages 56-68

  7. Learning from the Past to Dynamically Improve Search: A Case Study on the MOSP Problem

    • Hadrien Cambazard, Narendra Jussien
    Pages 69-80

  8. Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm

    • Yann Cooren, Amir Nakib, Patrick Siarry
    Pages 81-94

  9. Explicit and Emergent Cooperation Schemes for Search Algorithms

    • Teodor Gabriel Crainic, Michel Toulouse
    Pages 95-109

  10. Multiobjective Landscape Analysis and the Generalized Assignment Problem

    • Deon Garrett, Dipankar Dasgupta
    Pages 110-124

  11. Limited-Memory Techniques for Sensor Placement in Water Distribution Networks

    • William E. Hart, Jonathan W. Berry, Erik Boman, Cynthia A. Phillips, Lee Ann Riesen, Jean-Paul Watson
    Pages 125-137

  12. A Hybrid Clustering Algorithm Based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure

    • Yannis Marinakis, Magdalene Marinaki, Nikolaos Matsatsinis
    Pages 138-152

  13. Ant Colony Optimization and the Minimum Spanning Tree Problem

    • Frank Neumann, Carsten Witt
    Pages 153-166

  14. A Vector Assignment Approach for the Graph Coloring Problem

    • Takao Ono, Mutsunori Yagiura, Tomio Hirata
    Pages 167-176

  15. Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications

    • Lale Özbakır, Adil BaykasoÄŸlu, Sinem Kulluk
    Pages 177-191

  16. Tuning Local Search by Average-Reward Reinforcement Learning

    • Steven Prestwich
    Pages 192-205

  17. Evolution of Fitness Functions to Improve Heuristic Performance

    • Stephen Remde, Peter Cowling, Keshav Dahal, Nic Colledge
    Pages 206-219

  18. A Continuous Characterization of Maximal Cliques in k-Uniform Hypergraphs

    • Samuel Rota Bulò, Marcello Pelillo
    Pages 220-233

  19. Hybrid Heuristics for Multi-mode Resource-Constrained Project Scheduling

    • Celso Tchao, Simone L. Martins
    Pages 234-242

  20. Back Matter

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  1. Learning and Intelligent Optimization

Keywords

About this book

This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8–12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.

Editors and Affiliations

  • Dept. Computer Science, University of Bologna, Bologna, Italy

    Vittorio Maniezzo

  • Università degli Studi di Trento, Trento, Italy

    Roberto Battiti

  • Discrete Math and Complex Systems Department, Sandia National Laboratories, Albuquerque, USA

    Jean-Paul Watson

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