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2. Faster distance-based representative skyline and k-center along pareto front in the planeSergio Cabello, 2023, izvirni znanstveni članek Povzetek: We consider the problem of computing the distance-based representative skyline in the plane, a problem introduced by Tao, Ding, Lin and Pei and independently considered by Dupin, Nielsen and Talbi in the context of multi-objective optimization. Given a set $P$ of $n$ points in the plane and a parameter $k$, the task is to select $k$ points of the skyline defined by $P$ (also known as Pareto front for $P$) to minimize the maximum distance from the points of the skyline to the selected points. We show that the problem can be solved in $O(n \log h)$ time, where $h$ is the number of points in the skyline of $P$. We also show that the decision problem can be solved in $O(n \log k)$ time and the optimization problem can be solved in $O(n \log k + n \log\log n)$ time. This improves previous algorithms and is optimal for a large range of values of $k$. Ključne besede: geometric optimization, skyline, pareto front, clustering, k-center Objavljeno v DiRROS: 15.03.2024; Ogledov: 78; Prenosov: 36 Celotno besedilo (2,13 MB) Gradivo ima več datotek! Več... |
3. PS-AAS : portfolio selection for automated algorithm selection in black-box optimizationAna Kostovska, Gjorgjina Cenikj, Diederick Vermetten, Anja Janković, Ana Nikolikj, Urban Škvorc, Peter Korošec, Carola Doerr, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: automated algorithm selection, portfolio selection, black box optimization Objavljeno v DiRROS: 11.12.2023; Ogledov: 249; Prenosov: 94 Celotno besedilo (1,90 MB) Gradivo ima več datotek! Več... |
4. Sensitivity analysis of RF+clust for leave-one-problem-out performance predictionAna Nikolikj, Michal Pluhacek, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: automated performance prediction, autoML, single-objective black-box optimization, zero-shot learning Objavljeno v DiRROS: 13.11.2023; Ogledov: 315; Prenosov: 188 Celotno besedilo (4,94 MB) Gradivo ima več datotek! Več... |
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6. Algorithm instance footprint : separating easily solvable and challenging problem instancesAna Nikolikj, Sašo Džeroski, Mario Andrés Muñoz, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: black-box optimization, algorithms, problem instances, machine learning Objavljeno v DiRROS: 15.09.2023; Ogledov: 279; Prenosov: 183 Celotno besedilo (2,03 MB) Gradivo ima več datotek! Več... |
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8. DynamoRep : trajectory-based population dynamics for classification of black-box optimization problemsGjorgjina Cenikj, Gašper Petelin, Carola Doerr, Peter Korošec, Tome Eftimov, 2023, objavljeni znanstveni prispevek na konferenci Ključne besede: black-box single-objective optimization, optimization problem classification, problem representation, meta-learning Objavljeno v DiRROS: 30.08.2023; Ogledov: 324; Prenosov: 220 Celotno besedilo (650,13 KB) Gradivo ima več datotek! Več... |
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10. Tools for landscape analysis of optimisation problems in Procedural Content Generation for gamesVanessa Volz, Boris Naujoks, Pascal Kerschke, Tea Tušar, 2023, izvirni znanstveni članek Povzetek: The term Procedural Content Generation (PCG) refers to the (semi-)automatic generation of game content by algorithmic means, and its methods are becoming increasingly popular in game-oriented research and industry. A special class of these methods, which is commonly known as search-based PCG, treats the given task as an optimisation problem. Such problems are predominantly tackled by evolutionary algorithms. We will demonstrate in this paper that obtaining more information about the defined optimisation problem can substantially improve our understanding of how to approach the generation of content. To do so, we present and discuss three efficient analysis tools, namely diagonal walks, the estimation of high-level properties, as well as problem similarity measures. We discuss the purpose of each of the considered methods in the context of PCG and provide guidelines for the interpretation of the results received. This way we aim to provide methods for the comparison of PCG approaches and eventually, increase the quality and practicality of generated content in industry. Ključne besede: optimization, search-based procedural content generation, exploratory landscape analysis, Mario level generation Objavljeno v DiRROS: 24.02.2023; Ogledov: 410; Prenosov: 168 Celotno besedilo (745,09 KB) |