@prefix ocrer: <http://purl.org/net/OCRe/research.owl#> .
@prefix owl:   <http://www.w3.org/2002/07/owl#> .
@prefix scires: <http://vivoweb.org/ontology/scientific-research#> .
@prefix xsd:   <http://www.w3.org/2001/XMLSchema#> .
@prefix skos:  <http://www.w3.org/2004/02/skos/core#> .
@prefix rdfs:  <http://www.w3.org/2000/01/rdf-schema#> .
@prefix ocresd: <http://purl.org/net/OCRe/study_design.owl#> .
@prefix swo:   <http://www.ebi.ac.uk/efo/swo/> .
@prefix cito:  <http://purl.org/spar/cito/> .
@prefix geo:   <http://aims.fao.org/aos/geopolitical.owl#> .
@prefix ocresst: <http://purl.org/net/OCRe/statistics.owl#> .
@prefix dcterms: <http://purl.org/dc/terms/> .
@prefix vivo:  <http://vivoweb.org/ontology/core#> .
@prefix event: <http://purl.org/NET/c4dm/event.owl#> .
@prefix vann:  <http://purl.org/vocab/vann/> .
@prefix foaf:  <http://xmlns.com/foaf/0.1/> .
@prefix c4o:   <http://purl.org/spar/c4o/> .
@prefix fabio: <http://purl.org/spar/fabio/> .
@prefix vcard: <http://www.w3.org/2006/vcard/ns#> .
@prefix thkoeln: <http://cris.nrw/hisinone#> .
@prefix vitro: <http://vitro.mannlib.cornell.edu/ns/vitro/0.7#> .
@prefix vitro-public: <http://vitro.mannlib.cornell.edu/ns/vitro/public#> .
@prefix rdf:   <http://www.w3.org/1999/02/22-rdf-syntax-ns#> .
@prefix ocresp: <http://purl.org/net/OCRe/study_protocol.owl#> .
@prefix bibo:  <http://purl.org/ontology/bibo/> .
@prefix obo:   <http://purl.obolibrary.org/obo/> .
@prefix ro:    <http://purl.obolibrary.org/obo/ro.owl#> .

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        bibo:abstract             "The performance of optimization algorithms relies crucially on their  parameterizations. Finding good parameter settings is called algorithm tuning.  The sequential parameter optimization (SPOT) package for R is a toolbox for  tuning and understanding simulation and optimization algorithms. Model-based  investigations are common approaches in simulation and optimization. Sequential  parameter optimization has been developed, because there is a strong need for  sound statistical analysis of simulation and optimization algorithms. SPOT  includes methods for tuning based on classical regression and analysis of  variance techniques; tree-based models such as CART and random forest; Gaussian  process models (Kriging), and combinations of different meta-modeling  approaches. Using a simple simulated annealing algorithm, we will demonstrate  how optimization algorithms can be tuned using SPOT. The underling concepts of  the SPOT approach are explained. This includes key techniques such as  exploratory fitness landscape analysis and sensititvity analysis. Many examples  illustrate how SPOT can be used for understanding the performance of algorithms  and gaining insight into algorithm's behavior. Furthermore, we demonstrate how  SPOT can be used as an optimizer and how a sophisticated ensemble approach is  able to combine several meta models via stacking. This article exemplifies how  SPOT can be used for automatic and interactive tuning." ;
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        rdfs:label                "De.arXiv.org" ;
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