Learning and Intelligent Optimization (eBook) von Vittorio Maniezzo

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 volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8–12, 2007, in Trento, Italy. The motivation for the meeting is related to the current explosion in the number and variety of heuristic algorithms for hard optimization problems, which raises - merous interesting and challenging issues. Practitioners are confronted with the b- den of selecting the most appropriate method, in many cases through an expensive algorithm configuration and parameter-tuning process, and subject to a steep learning curve. Scientists seek theoretical insights and demand a sound experimental meth- ology for evaluating algorithms and assessing strengths and weaknesses. A necessary prerequisite for this effort is a clear separation between the algorithm and the expe- menter, who, in too many cases, is "in the loop" as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of "learning" about the performance of different techniques, and ways of using memory about algorithm behavior in the past to improve performance in the future. Intelligent learning schemes for mining the knowledge obtained from different runs or during a single run can - prove the algorithm development and design process and simplify the applications of high-performance optimization methods. Combinations of algorithms can further improve the robustness and performance of the individual components provided that sufficient knowledge of the relationship between problem instance characteristics and algorithm performance is obtained.