000 05632nam a22006495i 4500
001 978-3-319-50349-3
003 DE-He213
005 20210120143357.0
007 cr nn 008mamaa
008 161130s2016 gw | s |||| 0|eng d
020 _a9783319503493
_9978-3-319-50349-3
024 7 _a10.1007/978-3-319-50349-3
_2doi
050 4 _aQA76.9.A43
072 7 _aUMB
_2bicssc
072 7 _aCOM051300
_2bisacsh
072 7 _aUMB
_2thema
082 0 4 _a005.1
_223
245 1 0 _aLearning and Intelligent Optimization
_h[electronic resource] :
_b10th International Conference, LION 10, Ischia, Italy, May 29 -- June 1, 2016, Revised Selected Papers /
_cedited by Paola Festa, Meinolf Sellmann, Joaquin Vanschoren.
250 _a1st ed. 2016.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXI, 309 p. 74 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aTheoretical Computer Science and General Issues ;
_v10079
_aLecture Notes in Computer Science
_v10079
505 0 _aLearning 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.
520 _aThis 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.
650 0 _aAlgorithms.
650 0 _aComputer logic.
650 0 _aArtificial intelligence.
650 0 _aComputer science—Mathematics.
650 0 _aComputers.
650 0 _aComputer simulation.
650 1 4 _aAlgorithm Analysis and Problem Complexity.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I16021
650 2 4 _aLogics and Meanings of Programs.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I1603X
650 2 4 _aArtificial Intelligence.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I21000
650 2 4 _aDiscrete Mathematics in Computer Science.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I17028
650 2 4 _aComputation by Abstract Devices.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I16013
650 2 4 _aSimulation and Modeling.
_0https://scigraph.springernature.com/ontologies/product-market-codes/I19000
700 1 _aFesta, Paola.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSellmann, Meinolf.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aVanschoren, Joaquin.
_eeditor.
_0(orcid)0000-0001-7044-9805
_1https://orcid.org/0000-0001-7044-9805
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319503486
776 0 8 _iPrinted edition:
_z9783319503509
830 0 _aTheoretical Computer Science and General Issues ;
_v10079
830 0 _aLecture Notes in Computer Science ;
_v10079
856 4 0 _uhttps://doi.org/10.1007/978-3-319-50349-3
912 _aZDB-2-SCS
912 _aZDB-2-SXCS
912 _aZDB-2-LNC
942 _cEBK
999 _c57964
_d57964