Learning and Intelligent Optimization

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.

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.