This volume collects the accepted papers presented at the Learning and Intelligent OptimizatioN conference (LION 2007 II) held December 8a12, 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 qin the loopq as a crucial intelligent learning c- ponent. Both issues are related to designing and engineering ways of qlearningq 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.In order to test the performance of Reinforcement Learning for on-line parameter tuning in Reactive Tabu Search (RTS), we have implemented C++ functions for the Tabu Search method described in Sec.4.1and interfaced them to the Matlab LSPI implementation found in [16]. ... 5.2 5.1 Training Examples Generation The training examples are created by running the Tabu search algorithm over selectedanbsp;...

Title | : | Learning and Intelligent Optimization |

Author | : | Vittorio Maniezzo, Roberto Battiti, Jean-Paul Watson |

Publisher | : | Springer - 2008-12-17 |

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