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AuthorSpettel, Patrickdc.contributor.author
Date of accession2020-01-23T14:03:17Zdc.date.accessioned
Available in OPARU since2020-01-23T14:03:17Zdc.date.available
Year of creation2019dc.date.created
Date of first publication2020-01-23dc.date.issued
AbstractEvolution strategies are population-based randomized optimization strategies derived from a simplified model of Darwinian evolution. Being based on that principle, they are well-suited for black-box optimization. In that area, it is assumed that the objective function(s) and/or constraint function(s) can only be evaluated for given points in the parameter space but nothing else is known about these functions. In such scenarios, evolutionary algorithms in general, and evolution strategies in particular for real-valued spaces, are naturally well-suited. The focus of their development and analysis has initially mainly been on unconstrained problems. As a step toward a better understanding of evolution strategies for constrained problems, this thesis is a combination of theoretically guided algorithm design and theoretical analyses of evolution strategies for constrained optimization. In the first part, different algorithms are developed and empirically evaluated. Starting with linear constraints, an interior point evolution strategy with repair by projection is presented. For handling non-linear constraints, a second algorithm based on active covariance matrix adaptation is designed. The design of the algorithms is explained, they are empirically evaluated, and they are compared to other methods. The second part deals with theoretical analyses. A conically constrained linear optimization problem is considered. Evolution strategies with sigma-self-adaptation and cumulative step-size adaptation are theoretically investigated. For the analyses, closed-form approximations for the microscopic aspects are derived. They are further used to investigate the macroscopic behavior of the algorithms (mean value dynamics and behavior in the steady state). The theoretically derived expressions are compared to real algorithm runs for showing the approximation quality.dc.description.abstract
Languageen_USdc.language.iso
PublisherUniversität Ulmdc.publisher
LicenseStandarddc.rights
Link to license texthttps://oparu.uni-ulm.de/xmlui/license_v3dc.rights.uri
KeywordEvolution strategiesdc.subject
KeywordBlack-box optimization benchmarkingdc.subject
KeywordCovariance Matrix Self-Adaptation Evolution Strategydc.subject
KeywordActive Matrix Adaptation Evolution Strategydc.subject
KeywordRepair by projectiondc.subject
KeywordConically constrained problemdc.subject
KeywordTheoretical analysesdc.subject
KeywordSelf-adaptationdc.subject
KeywordCumulative step size adaptationdc.subject
Dewey Decimal GroupDDC 500 / Natural sciences & mathematicsdc.subject.ddc
Dewey Decimal GroupDDC 570 / Life sciencesdc.subject.ddc
LCSHConstrained optimizationdc.subject.lcsh
LCSHBlack boxdc.subject.lcsh
LCSHEvolution (Biology)dc.subject.lcsh
TitleEvolution strategies for constrained optimizationdc.title
Resource typeDissertationdc.type
Date of acceptance2019-11-22dcterms.dateAccepted
RefereeBeyer, Hans-Georgdc.contributor.referee
RefereeSchöning, Uwedc.contributor.referee
RefereeMeyer-Nieberg, Siljadc.contributor.referee
DOIhttp://dx.doi.org/10.18725/OPARU-24371dc.identifier.doi
PPN1688526838dc.identifier.ppn
URNhttp://nbn-resolving.de/urn:nbn:de:bsz:289-oparu-24434-4dc.identifier.urn
GNDKovarianzmatrixdc.subject.gnd
FacultyFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.affiliationGeneral
InstitutionInstitut für Theoretische Informatikuulm.affiliationSpecific
Grantor of degreeFakultät für Ingenieurwissenschaften, Informatik und Psychologieuulm.thesisGrantor
DCMI TypeTextuulm.typeDCMI
CategoryPublikationenuulm.category
In cooperation withFachhochschule Vorarlberguulm.cooperation
Bibliographyuulmuulm.bibliographie


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