Learning and Intelligent Optimization: 10th International Conference, Lion 10, Ischia, Italy, May 29 -- June 1, 2016, Revised Selected Papers

ISBN-13: 9783319503486 / Angielski / Miękka / 2016 / 309 str.

Paola Festa; Meinolf Sellmann; Joaquin Vanschoren

Opis

This book constitutes the thoroughly refereed post-conference proceedings of the 10th International Conference on Learning and Optimization, LION 10, which was held on Ischia, Italy, in May/June 2016.The 14 full papers presented together with 9 short papers and 2 GENOPT papers were carefully reviewed and selected from 47 submissions.

Spis treści

Learning a stopping criteria for Local Search.- Surrogate Assisted Feature Computation for Continuous Problems.- MO-ParamILS: A Multi-objective Automatic Algorithm Configuration Framework.- Evolving Instances for Maximizing Performance Differences of State-of-The-Art Inexact TSP Solvers.- Extreme Reactive Portfolio (XRP): Tuning an Algorithm Population for Global Optimization.- Bounding the Search Space of the Population Harvest Cutting Problem with Multiple Size Stock Selection.- Designing and comparing multiple portfolios of parameter configurations for online algorithm selection.- Portfolios of Subgraph Isomorphism Algorithms.- Structure-preserving Instance Generation.- Feature Selection using Tabu Search with Learning Memory: Learning Tabu Search.- The Impact of Automated Algorithm Configuration on the Scaling Behaviour of State-of-the-art Inexact TSP Solvers.- Requests Management for Smartphone-based Matching Applications using a Multi-Agent Approach.- Self-Organizing Neural Network for Adaptive Operator Selection in Evolutionary Search.- Quantifying the Similarity of Algorithm Configurations.- Neighborhood synthesis from an ensemble of MIP and CP models.- Parallelizing Constraint Solvers for Hard RCPSP Instances.- Characterization of neighborhood behaviours in a multi-neighborhood local search algorithm.- Constraint Programming and Machine Learning for Interactive Soccer Analysis.- A Matheuristic Approach for the p-Cable Trench Problem.- An Empirical Study of Per-Instance Algorithm Scheduling.- Dynamic strategy to diversify search using history map in parallel solving.- Faster Model Based Optimization through Resource Aware Scheduling Strategies.- Risk-Averse Anticipation for Dynamic Vehicle Routing.- Solving GENOPT problems with the use of ExaMin solver.- Hybridisation of Evolutionary Algorithms through Hyper-heuristics for Global Continuous Optimisation.