Evolutionary Dynamics of Extremal Optimization -- Evolutionary Dynamics of Extremal Optimization -- A Variable Neighborhood Descent Search Algorithm for Delay-Constrained Least-Cost Multicast Routing -- Expeditive Extensions of Evolutionary Bayesian Probabilistic Neural Networks -- New Bounds on the Clique Number of Graphs Based on Spectral Hypergraph Theory -- Beam-ACO Based on Stochastic Sampling: A Case Study on the TSP with Time Windows -- Flexible Stochastic Local Search for Haplotype Inference -- A Knowledge Discovery Approach to Understanding Relationships between Scheduling Problem Structure and Heuristic Performance -- Fitness Landscape Analysis for the Resource Constrained Project Scheduling Problem -- An ACO-Based Reactive Framework for Ant Colony Optimization: First Experiments on Constraint Satisfaction Problems -- Selection of Heuristics for the Job-Shop Scheduling Problem Based on the Prediction of Gaps in Machines -- Position-Guided Tabu Search Algorithm for the Graph Coloring Problem -- Corridor Selection and Fine Tuning for the Corridor Method -- Dynamic Multi-Armed Bandits and Extreme Value-Based Rewards for Adaptive Operator Selection in Evolutionary Algorithms -- Comparison of Coarsening Schemes for Multilevel Graph Partitioning -- Cooperative Strategies and Reactive Search: A Hybrid Model Proposal -- Study of the Influence of the Local Search Method in Memetic Algorithms for Large Scale Continuous Optimization Problems -- MALIOB Workshop Papers -- Neural Network Pairwise Interaction Fields for Protein Model Quality Assessment -- A Graph-Based Semi-supervised Algorithm for Protein Function Prediction from Interaction Maps -- Substitution Matrices and Mutual Information Approaches to Modeling Evolution.

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.).