121. Opt2Vec - a continuous optimization problem representation based on the algorithm's behavior : A case study on problem classificationPeter Korošec, Tome Eftimov, 2024, izvirni znanstveni članek Povzetek: Characterization of the optimization problem is a crucial task in many recent optimization research topics (e.g., explainable algorithm performance assessment, and automated algorithm selection and configuration). The state-of-the-art approaches use exploratory landscape analysis to represent the optimization problem, where for each one, a set of features is extracted using a set of candidate solutions sampled by a sampling strategy over the whole decision space. This paper proposes a novel representation of continuous optimization problems by encoding the information found in the interaction between an algorithm and an optimization problem. The new problem representation is learned using the information from the states/positions in the optimization run trajectory (i.e., the candidate solutions visited by the algorithm). With the novel representation, the problem can be characterized dynamically during the optimization run, instead of using a set of candidate solutions from the whole decision space that have never been observed by the algorithm. The novel optimization problem representation is called Opt2Vec and uses an autoencoder type of neural network to encode the information found in the interaction between an optimization algorithm and optimization problem into an embedded subspace. The Opt2Vec representation efficiency is shown by enabling different optimization problems to be successfully identified using only the information obtained from the optimization run trajectory.
Objavljeno v DiRROS: 11.12.2024; Ogledov: 82; Prenosov: 23
 Celotno besedilo (4,42 MB) |
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123. Using machine learning methods to assess module performance contribution in modular optimization frameworksAna Kostovska, Diederick Vermetten, Peter Korošec, Sašo Džeroski, Carola Doerr, Tome Eftimov, 2024, izvirni znanstveni članek Povzetek: Modular algorithm frameworks not only allow for combinations never tested in manually selected algorithm portfolios, but they also provide a structured approach to assess which algorithmic ideas are crucial for the observed performance of algorithms. In this study, we propose a methodology for analyzing the impact of the different modules on the overall performance. We consider modular frameworks for two widely used families of derivative-free black-box optimization algorithms, the Covariance Matrix Adaptation Evolution Strategy (CMA-ES) and differential evolution (DE). More specifically, we use performance data of 324 modCMA-ES and 576 modDE algorithm variants (with each variant corresponding to a specific configuration of modules) obtained on the 24 BBOB problems for 6 different runtime budgets in 2 dimensions. Our analysis of these data reveals that the impact of individual modules on overall algorithm performance varies significantly. Notably, among the examined modules, the elitism module in CMA-ES and the linear population size reduction module in DE exhibit the most significant impact on performance. Furthermore, our exploratory data analysis of problem landscape data suggests that the most relevant landscape features remain consistent regardless of the configuration of individual modules, but the influence that these features have on regression accuracy varies. In addition, we apply classifiers that exploit feature importance with respect to the trained models for performance prediction and performance data, to predict the modular configurations of CMA-ES and DE algorithm variants. The results show that the predicted configurations do not exhibit a statistically significant difference in performance compared to the true configurations, with the percentage varying depending on the setup (from 49.1% to 95.5% for modCMA and 21.7% to 77.1% for DE)
Ključne besede: evolutionary computation, modular algorithm frameworks, DE
Objavljeno v DiRROS: 11.12.2024; Ogledov: 97; Prenosov: 36
Celotno besedilo (1,37 MB)
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124. Geometric matching and bottleneck problemsSergio Cabello, Siu-Wing Cheng, Otfried Cheong, Christian Knauer, 2024, objavljeni znanstveni prispevek na konferenci Povzetek: Let $P$ be a set of at most $n$ points and let $R$ be a set of at most $n$ geometric ranges, such as disks or rectangles, where each $p \in P$ has an associated supply $s_{p} > 0$, and each $r \in R$ has an associated demand $d_{r} > 0$. A (many-to-many) matching is a set ${\mathcal A}$ of ordered triples $(p,r,a_{pr}) \in P \times R \times {\mathbb R}_{>0}$ such that $p \in r$ and the $a_{pr}$'s satisfy the constraints given by the supplies and demands. We show how to compute a maximum matching, that is, a matching maximizing $\sum_{(p,r,a_{pr}) \in {\mathcal A}} a_{pr}$. Using our techniques, we can also solve minimum bottleneck problems, such as computing a perfect matching between a set of $n$ red points $P$ and a set of $n$ blue points $Q$ that minimizes the length of the longest edge. For the $L_\infty$-metric, we can do this in time $O(n^{1+\varepsilon})$ in any fixed dimension, for the $L_2$-metric in the plane in time $O(n^{4/3 + \varepsilon})$, for any $\varepsilon > 0$.
Ključne besede: many-to-many matching, bipartite, planar, geometric matching, approximation
Objavljeno v DiRROS: 11.12.2024; Ogledov: 66; Prenosov: 28
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125. Izpad dohodka v gozdarskem sektorju v obdobju od 16. 3. 2020 do 30. 6. 2020Nike Krajnc, Darja Stare, Špela Ščap, Matevž Triplat, 2020, strokovni članek Ključne besede: covid-19, tržne razmere, les, gozdarstvo, izpad dohodka
Objavljeno v DiRROS: 11.12.2024; Ogledov: 77; Prenosov: 23
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130. Kako bo država pomagala lastnikom gozdov, ki imajo izpad dohodka zaradi epidemije COVID-19Nike Krajnc, 2020, strokovni članek Ključne besede: gozdovi, gozdarstvo, epidemija, Covid-19, lastniki gozdov, sanitarna sečnja, finančno nadomestilo, odkup lesa
Objavljeno v DiRROS: 11.12.2024; Ogledov: 75; Prenosov: 22
Celotno besedilo (1,87 MB) |
