Learning and Intelligent Optimization: Second International Conference, LION 2007 II Trento, Italy, December 8-12, 2007 Selected Papers: 5313 (Lecture Notes in Computer Science) - Softcover

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.

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.