201. 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: 115; Prenosov: 45
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202. 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: 79; Prenosov: 33
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203. 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: 95; Prenosov: 29
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208. 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: 88; Prenosov: 29
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209. Kmetijska in gozdarska tehnika v času pandemijeMarjan Dolenšek, Robert Jerončič, 2020, strokovni članek Ključne besede: gozdovi, gozdarstvo, epidemija, Covid-19, kmetijska tehnika, gozdarska tehnika, kmetijstvo, registracija motornih vozil, varnost pri delu
Objavljeno v DiRROS: 11.12.2024; Ogledov: 90; Prenosov: 28
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210. Citizen science platforms can effectively support early detection of invasive alien species according to species traitsPablo González-Moreno, Ana A. Anđelković, Tim Adriaens, Christophe Botella, Jakovos Demetriou, Rita Bastos, Sandro Bertolino, Celia López-Cañizares, Franz Essl, Živa Fišer, Maarten De Groot, 2024, izvirni znanstveni članek Povzetek: Early detection and rapid response are essential to deal effectively with new introductions of invasive alien species (IAS). Citizen science platforms for opportunistic recording of species are increasingly popular, and there is potential to harvest their data for early detection of IAS, but this has not been tested.
We evaluated the potential of data from existing citizen science platforms for early detection of IAS by obtaining 687 first records of species from 30 European countries where there was both an official first record (i.e. published in scientific literature or by a government agency) and a record in a citizen science platform. We tested how the difference between the two (time lag) was related to species traits, popularity in citizen science platforms, public and research attention and regulatory status.
We found that for 50% of the time lag records, citizen science platforms reported IAS earlier than or in the same year as the official databases. Although we cannot determine causality (the first official record could have been from a citizen science platform, or contemporaneous with it), this demonstrates that citizen science platforms are effective for IAS early detection.
Time lags were largely affected by species traits. Compared with official records, vertebrates were more likely to have earlier records on citizen science platforms, than plants or invertebrates. Greater popularity of the IAS in citizen science platforms and its observation in neighbouring countries resulted in earlier citizen science reporting. In contrast, inclusion in the EU priority list resulted in earlier official recording, reflecting the efficacy of targeted surveillance programmes. However, time lags were not affected by the overall activity of citizen platforms per country.
Synthesis and applications. Multi-species citizen science platforms for reporting nature sightings are a valuable source of information on early detection of IAS even though they are not specifically designed for this purpose. We recommend that IAS surveillance programmes should be better connected with citizen science platforms, including greater acknowledgement of the role of citizen scientists and better data flow from smaller citizen science initiatives into global databases, to support efficient early detection.
Ključne besede: invasive species, citizen science, early detection
Objavljeno v DiRROS: 11.12.2024; Ogledov: 86; Prenosov: 39
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