Learning and Intelligent Optimization

10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, Revised Selected Papers

AvPaola Festa,Meinolf Sellmann

Häftad, Engelska, 2016

Del 10079 i serien Lecture Notes in Computer Science

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Beskrivning

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.

Produktinformation

Utgivningsdatum:2016-12-06
Mått:155 x 235 x 18 mm
Vikt:493 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:309
Förlag:Springer International Publishing AG
ISBN:9783319503486

Utforska kategorier

Systemvetenskap och AI inom Data och IT
Artificiell intelligens inom Data och IT
Programmeringsböcker inom Data och IT

Innehållsförteckning

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.