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1	Evolutionary many-objective optimisation : pushing the boundaries Li, Miqing January 2015 (has links) Many-objective optimisation poses great challenges to evolutionary algorithms. To start with, the ineffectiveness of the Pareto dominance relation, which is the most important criterion in multi-objective optimisation, results in the underperformance of traditional Pareto-based algorithms. Also, the aggravation of the conflict between proximity and diversity, along with increasing time or space requirement as well as parameter sensitivity, has become key barriers to the design of effective many-objective optimisation algorithms. Furthermore, the infeasibility of solutions' direct observation can lead to serious difficulties in algorithms' performance investigation and comparison. In this thesis, we address these challenges, aiming to make evolutionary algorithms as effective in many-objective optimisation as in two- or three-objective optimisation. First, we significantly enhance Pareto-based algorithms to make them suitable for many-objective optimisation by placing individuals with poor proximity into crowded regions so that these individuals can have a better chance to be eliminated. Second, we propose a grid-based evolutionary algorithm which explores the potential of the grid to deal with many-objective optimisation problems. Third, we present a bi-goal evolution framework that converts many objectives of a given problem into two objectives regarding proximity and diversity, thus creating an optimisation problem in which the objectives are the goals of the search process itself. Fourth, we propose a comprehensive performance indicator to compare evolutionary algorithms in optimisation problems with various Pareto front shapes and any objective dimensionality. Finally, we construct a test problem to aid the visual investigation of evolutionary search, with its Pareto optimal solutions in a two-dimensional decision space having similar distribution to their images in a higher-dimensional objective space. The work reported in this thesis is the outcome of innovative attempts at addressing some of the most challenging problems in evolutionary many-objective optimisation. This research has not only made some of the existing approaches, such as Pareto-based or grid-based algorithms that were traditionally regarded as unsuitable, now effective for many-objective optimisation, but also pushed other important boundaries with novel ideas including bi-goal evolution, a comprehensive performance indicator and a test problem for visual investigation. All the proposed algorithms have been systematically evaluated against existing state of the arts, and some of these algorithms have already been taken up by researchers and practitioners in the field. 005.1
2	Optimierung von Messinstrumenten im Large-scale Assessment Hecht, Martin 21 July 2015 (has links) Messinstrumente stellen in der wissenschaftlichen Forschung ein wesentliches Element zur Erkenntnisgewinnung dar. Das Besondere an Messinstrumenten im Large-scale Assessment in der Bildungsforschung ist, dass diese normalerweise für jede Studie neu konstruiert werden und dass die Testteilnehmer verschiedene Versionen des Tests bekommen. Hierbei ergeben sich potentielle Gefahren für die Akkuratheit und Validität der Messung. Um solche Gefahren zu minimieren, sollten (a) die Ursachen für Verzerrungen der Messung und (b) mögliche Strategien zur Optimierung der Messinstrumente eruiert werden. Deshalb wird in der vorliegenden Dissertation spezifischen Fragestellungen im Rahmen dieser beiden Forschungsanliegen nachgegangen. / Measurement instruments are essential elements in the acquisition of knowledge in scientific research. Special features of measurement instruments in large-scale assessments of student achievement are their frequent reconstruction and the usage of different test versions. Here, threats for the accuracy and validity of the measurement may emerge. To minimize such threats, (a) sources for potential bias of measurement and (b) strategies to optimize measuring instruments should be explored. Therefore, the present dissertation investigates several specific topics within these two research areas. Psychologie Bildungsforschung Optimierung Kontexteffekte Bearbeitungszeit Blockpaarbalance Designeffekte Designbalancierung Generalisierte lineare Mischmodelle IQB IRT Lineare Mischmodelle Messmodell Messinstrument Multi-Matrix Design Parameterschätzung Parameterverzerrung Positionsbalance Rasch-Modell Testhefteffekte Testheftschwierigkeit Testheftleichtigkeit Testdesign Unvollständige Blockdesigns Positionseffekte Schulleistungsstudien context effects optimization linear mixed models generalized linear mixed models response time educational assessment cluster pair balance design effects design balance item response theory measurement model measurement instrument parameter estimation parameter bias position balance psychology Rasch model 1PL model booklet effects booklet difficulty booklet easiness test design booklet design incomplete block design multiple matrix sampling position effects large-scale assessment 150 Psychologie 11 Psychologie CM 3000 CM 3200 ddc:150

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Evolutionary many-objective optimisation : pushing the boundaries

Optimierung von Messinstrumenten im Large-scale Assessment