Learning and Intelligent Optimization: Designing, Implementing and Analyzing Effective Heuristics

1. Front Matter

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2. Evolutionary Dynamics of Extremal Optimization

   1. Evolutionary Dynamics of Extremal Optimization

      • Stefan Boettcher

      Pages 1-14

   2. A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing

      • Rong Qu, Ying Xu, Graham Kendall

      Pages 15-29

   3. Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks

      • Vasileios L. Georgiou, Sonia Malefaki, Konstantinos E. Parsopoulos, Philipos D. Alevizos, Michael N. Vrahatis

      Pages 30-44

   4. New Bounds on the Clique Number of Graphs Based on Spectral Hypergraph Theory

      • Samuel Rota Bulò, Marcello Pelillo

      Pages 45-58

   5. Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows

      • Manuel López-Ibáñez, Christian Blum

      Pages 59-73

   6. Flexible Stochastic Local Search for Haplotype Inference

      • Luca Di Gaspero, Andrea Roli

      Pages 74-88

   7. A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance

      • Kate A. Smith-Miles, Ross J. W. James, John W. Giffin, Yiqing Tu

      Pages 89-103

   8. Fitness Landscape Analysis for the Resource Constrained Project Scheduling Problem

      • Jens Czogalla, Andreas Fink

      Pages 104-118

   9. An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems

      • Madjid Khichane, Patrick Albert, Christine Solnon

      Pages 119-133

   10. Selection of Heuristics for the Job-Shop Scheduling Problem Based on the Prediction of Gaps in Machines

       • Pedro Abreu, Carlos Soares, Jorge M. S. Valente

       Pages 134-147

   11. Position-Guided Tabu Search Algorithm for the Graph Coloring Problem

       • Daniel Cosmin Porumbel, Jin-Kao Hao, Pascale Kuntz

       Pages 148-162

   12. Corridor Selection and Fine Tuning for the Corridor Method

       • Marco Caserta, Stefan Voß

       Pages 163-175

   13. Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms

       • Álvaro Fialho, Luis Da Costa, Marc Schoenauer, Michèle Sebag

       Pages 176-190

   14. Comparison of Coarsening Schemes for Multilevel Graph Partitioning

       • Cédric Chevalier, Ilya Safro

       Pages 191-205

   15. Cooperative Strategies and Reactive Search: A Hybrid Model Proposal

       • Antonio D. Masegosa, Franco Mascia, David Pelta, Mauro Brunato

       Pages 206-220

   16. Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems

       • Daniel Molina, Manuel Lozano, Francisco Herrera

       Pages 221-234

3. MALIOB Workshop Papers

   1. Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment

      • Alberto J. M. Martin, Alessandro Vullo, Gianluca Pollastri

      Pages 235-248

   2. A Graph-Based Semi-supervised Algorithm for Protein Function Prediction from Interaction Maps

      • Valerio Freschi

      Pages 249-258

   3. Substitution Matrices and Mutual Information Approaches to Modeling Evolution

      • Stephan Kitchovitch, Yuedong Song, Richard van der Wath, Pietro Liò

      Pages 259-272

LION 3, the Third International Conference on Learning and Intelligent Op- mizatioN, was held during January 14–18 in Trento, Italy. The LION series of conferences provides a platform for researchers who are interested in the int- section of e?cient optimization techniques and learning. It is aimed at exploring the boundaries and uncharted territories between machine learning, arti?cial intelligence, mathematical programming and algorithms for hard optimization problems. The considerable interest in the topics covered by LION was re?ected by the overwhelming number of 86 submissions, which almost doubled the 48 subm- sions received for LION’s second edition in December 2007. As in the ?rst two editions, the submissions to LION 3 could be in three formats: (a) original novel and unpublished work for publication in the post-conference proceedings, (b) extended abstracts of work-in-progressor a position statement, and (c) recently submitted or published journal articles for oral presentations. The 86 subm- sions received include 72, ten, and four articles for categories (a), (b), and (c), respectively.

• Variable
• algorithms
• evolutionary algorithm
• heuristics
• intelligence
• learning
• optimization

• IRIDIA, CoDE, Université Libre de Bruxelles, Brussels, Belgium

  Thomas Stützle

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