Learning and intelligent optimization : designing, implementing and analyzing effective heuristics : third international conference, LION 3, Trento, Italy, January 14-18, 2009 : selected papers

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 As in the ?rst two editions, the submissions to LION 3 could be in three formats: (a) original novel and unpublished work for publication in the post-conference proceedings, (b) extended abstracts of work-in-progressor a position statement, and (c) recently submitted or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. Front Matter....Pages - Evolutionary Dynamics of Extremal Optimization....Pages 1-14 A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing....Pages 15-29 Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks....Pages 30-44 New Bounds on the Clique Number of Graphs Based on Spectral Hypergraph Theory....Pages 45-58 Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows....Pages 59-73 Flexible Stochastic Local Search for Haplotype Inference....Pages 74-88 A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance....Pages 89-103 Fitness Landscape Analysis for the Resource Constrained Project Scheduling Problem....Pages 104-118 An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems....Pages 119-133 Selection of Heuristics for the Job-Shop Scheduling Problem Based on the Prediction of Gaps in Machines....Pages 134-147 Position-Guided Tabu Search Algorithm for the Graph Coloring Problem....Pages 148-162 Corridor Selection and Fine Tuning for the Corridor Method....Pages 163-175 Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms....Pages 176-190 Comparison of Coarsening Schemes for Multilevel Graph Partitioning....Pages 191-205 Cooperative Strategies and Reactive Search: A Hybrid Model Proposal....Pages 206-220 Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems....Pages 221-234 Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment....Pages 235-248 A Graph-Based Semi-supervised Algorithm for Protein Function Prediction from Interaction Maps....Pages 249-258 Substitution Matrices and Mutual Information Approaches to Modeling Evolution....Pages 259-272 Back Matter....Pages - This book constitutes the thoroughly refereed post-conference proceedings of the Third International Conference on Learning and Intelligent Optimization, LION 2009 III, held in Trento, Italy, in January 2009. The 15 revised full papers, one extended abstract and two poster sessions were carefully reviewed and selected from 86 submissions for inclusion in the book. The papers cover current issues of stochastic local search methods and meta-heuristics, hybridizations of constraint and mathematical programming with meta-heuristics, supervised, unsupervised and reinforcement learning applied to heuristic search, reactive search (online self-tuning methods), algorithm portfolios and off-line tuning methods, algorithms for dynamic, stochastic and multi-objective problems, interface(s) between discrete and continuous optimization, experimental analysis and modeling of algorithms, theoretical foundations, parallelization of optimization algorithms, memory-based optimization, prohibition-based methods (tabu search), memetic algorithms, evolutionary algorithms, dynamic local search, iterated local search, variable neighborhood search and swarm intelligence methods (ant colony optimization, particle swarm optimization etc.)