Learning and Intelligent Optimization

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. The papers address all fields between machine learning, artificial intelligence, mathematical programming and algorithms for hard optimization problems. Special focus is given to new ideas and methods; challenges and opportunities in various application areas; general trends, and specific developments.

Inhalt

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.