Nested Partitioning for the Minimum Energy Broadcast Problem -- An Adaptive Memory-Based Approach Based on Partial Enumeration -- Learning While Optimizing an Unknown Fitness Surface -- On Effectively Finding Maximal Quasi-cliques in Graphs -- Improving the Exploration Strategy in Bandit Algorithms -- Learning from the Past to Dynamically Improve Search: A Case Study on the MOSP Problem -- Image Thresholding Using TRIBES, a Parameter-Free Particle Swarm Optimization Algorithm -- Explicit and Emergent Cooperation Schemes for Search Algorithms -- Multiobjective Landscape Analysis and the Generalized Assignment Problem -- Limited-Memory Techniques for Sensor Placement in Water Distribution Networks -- A Hybrid Clustering Algorithm Based on Honey Bees Mating Optimization and Greedy Randomized Adaptive Search Procedure -- Ant Colony Optimization and the Minimum Spanning Tree Problem -- A Vector Assignment Approach for the Graph Coloring Problem -- Rule Extraction from Neural Networks Via Ant Colony Algorithm for Data Mining Applications -- Tuning Local Search by Average-Reward Reinforcement Learning -- Evolution of Fitness Functions to Improve Heuristic Performance -- A Continuous Characterization of Maximal Cliques in k-Uniform Hypergraphs -- Hybrid Heuristics for Multi-mode Resource-Constrained Project Scheduling.

This book constitutes the thoroughly refereed post-conference proceedings of the Second International Conference on Learning and Intelligent Optimization, LION 2007 II, held in Trento, Italy, in December 2007. The 18 revised full papers were carefully reviewed and selected from 48 submissions for inclusion in the book. The papers cover current issues of machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems and are organized in topical sections on improving optimization through learning, variable neighborhood search, insect colony optimization, applications, new paradigms, cliques, stochastic optimization, combinatorial optimization, fitness and landscapes, and particle swarm optimization.