IA Scholar Query: U. Lengler
https://scholar.archive.org/
Internet Archive Scholar query results feedeninfo@archive.orgThu, 15 Sep 2022 00:00:00 GMTfatcat-scholarhttps://scholar.archive.org/help1440Cover and Hitting Times of Hyperbolic Random Graphs
https://scholar.archive.org/work/ptgv75iy35gfbb3xwkm5bgkkpa
We study random walks on the giant component of Hyperbolic Random Graphs (HRGs), in the regime when the degree distribution obeys a power law with exponent in the range (2,3). In particular, we focus on the expected times for a random walk to hit a given vertex or visit, i.e. cover, all vertices. We show that up to multiplicative constants: the cover time is n(log n)², the maximum hitting time is nlog n, and the average hitting time is n. The first two results hold in expectation and a.a.s. and the last in expectation (with respect to the HRG). We prove these results by determining the effective resistance either between an average vertex and the well-connected "center" of HRGs or between an appropriately chosen collection of extremal vertices. We bound the effective resistance by the energy dissipated by carefully designed network flows associated to a tiling of the hyperbolic plane on which we overlay a forest-like structure.Marcos Kiwi, Markus Schepers, John Sylvester, Amit Chakrabarti, Chaitanya Swamywork_ptgv75iy35gfbb3xwkm5bgkkpaThu, 15 Sep 2022 00:00:00 GMTStudiengangkoordination in der wissenschaftlichen Weiterbildung – Zentrale Schnittstellenfunktion und vielfältige Aufgabenfelder
https://scholar.archive.org/work/ltqu4krm35abjevwsdfts6aylu
This doctoral dissertation on "Program coordination in continuing education at universities" pursues the empirical determination of the scope of professional tasks and the function as central interface in this kind of program coordination. It specifically focuses on program coordination in decentralized study programs at universities and is contextualized in research on adult education professions and research on academic professions. The work therefore significantly contributes therefore to an adult educational and organizational contemplation of the new professional field: continuing education at universities. Formally, the thesis incudes the introductory text as well as six published articles in journals and anthologies: - an article summarizing the scopes of professional tasks of program coordination as the result of an empirical-exploratory study, - two articles which systematically locate program coordination in the context of adult education management or professional work in higher education (third mission), - three articles, which exemplarily focus in depth on guidance and counselling (student support) and cooperation as important tasks of program coordination in continuing education. The introductory text provides theoretically well-founded insights into the field of continuing education at universities (specifics, professionalization) and the university as a specific kind of organization. This cumulative thesis thus contributes to the determination and differentiation measurement of a vocational field (program coordination) in a specific organizational context (continuing education at universities).Heike Rundnagel, Seitter, Wolfgang (Prof. Dr.), Erziehungswissenschaftwork_ltqu4krm35abjevwsdfts6ayluWed, 07 Sep 2022 00:00:00 GMTDagstuhl Reports, Volume 12, Issue 2, February 2022, Complete Issue
https://scholar.archive.org/work/scntyrlsivecdcac4psbic4qzy
Dagstuhl Reports, Volume 12, Issue 2, February 2022, Complete Issuework_scntyrlsivecdcac4psbic4qzyTue, 23 Aug 2022 00:00:00 GMTInfluence of Binomial Crossover on Approximation Error of Evolutionary Algorithms
https://scholar.archive.org/work/7xe4wo7gtredrm5w3k5anykbce
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms. To make the first attempt to reveal the function of binomial crossover, this paper aims to answer whether it can reduce the approximation error of evolutionary algorithms. By investigating the expected approximation error and the probability of not finding the optimum, we conduct a case study comparing two evolutionary algorithms with and without binomial crossover on two classical benchmark problems: OneMax and Deceptive. It is proven that using binomial crossover leads to the dominance of transition matrices. As a result, the algorithm with binomial crossover asymptotically outperforms that without crossover on both OneMax and Deceptive, and outperforms on OneMax, however, not on Deceptive. Furthermore, an adaptive parameter strategy is proposed which can strengthen the superiority of binomial crossover on Deceptive.Cong Wang and Jun He and Yu Chen and Xiufen Zouwork_7xe4wo7gtredrm5w3k5anykbceMon, 15 Aug 2022 00:00:00 GMTInfluence of Binomial Crossover on Approximation Error of Evolutionary Algorithms
https://scholar.archive.org/work/ekxpifckxbf6tkwc7e426g7pw4
Although differential evolution (DE) algorithms perform well on a large variety of complicated optimization problems, only a few theoretical studies are focused on the working principle of DE algorithms. To make the first attempt to reveal the function of binomial crossover, this paper aims to answer whether it can reduce the approximation error of evolutionary algorithms. By investigating the expected approximation error and the probability of not finding the optimum, we conduct a case study comparing two evolutionary algorithms with and without binomial crossover on two classical benchmark problems: OneMax and Deceptive. It is proven that using binomial crossover leads to the dominance of transition matrices. As a result, the algorithm with binomial crossover asymptotically outperforms that without crossover on both OneMax and Deceptive, and outperforms on OneMax, however, not on Deceptive. Furthermore, an adaptive parameter strategy is proposed which can strengthen the superiority of binomial crossover on Deceptive.Cong Wang, Jun He, Yu Chen, Xiufen Zouwork_ekxpifckxbf6tkwc7e426g7pw4Wed, 10 Aug 2022 00:00:00 GMTIntuitive Analyses via Drift Theory
https://scholar.archive.org/work/ics3ebh77jchrdqtgm7uo6mg4i
Drift theory is an intuitive tool for reasoning about random processes: It allows turning expected stepwise changes into expected first-hitting times. While drift theory is used extensively by the community studying randomized search heuristics, it has seen hardly any applications outside of this field, in spite of many research questions that can be formulated as first-hitting times. We state the most useful drift theorems and demonstrate their use for various randomized processes, including the coupon collector process, winning streaks, approximating vertex cover, and a random sorting algorithm. We also consider processes without expected stepwise change and give theorems based on drift theory applicable in such scenarios. We use these theorems for the analysis of the gambler's ruin process, for a coloring algorithm, for an algorithm for 2-SAT, and for a version of the Moran process without bias. A final tool we present is a tight theorem for processes on finite state spaces, which we apply to the Moran process. We aim to enable the reader to apply drift theory in their own research to derive accessible proofs and to teach it as a simple tool for the analysis of random processes.Andreas Göbel and Timo Kötzing and Martin S. Krejcawork_ics3ebh77jchrdqtgm7uo6mg4iMon, 18 Jul 2022 00:00:00 GMTCover and Hitting Times of Hyperbolic Random Graphs
https://scholar.archive.org/work/kyt6tb5avngkrjjg2pfvzhocwa
We study random walks on the giant component of Hyperbolic Random Graphs (HRGs), in the regime when the degree distribution obeys a power law with exponent in the range (2,3). In particular, we focus on the expected times for a random walk to hit a given vertex or visit, i.e. cover, all vertices. We show that up to multiplicative constants: the cover time is n(log n)^2, the maximum hitting time is nlog n, and the average hitting time is n. The first two results hold in expectation and a.a.s. and the last in expectation (with respect to the HRG). We prove these results by determining the effective resistance either between an average vertex and the well-connected "center" of HRGs or between an appropriately chosen collection of extremal vertices. We bound the effective resistance by the energy dissipated by carefully designed network flows associated to a tiling of the hyperbolic plane on which we overlay a forest-like structure.Marcos Kiwi, Markus Schepers, John Sylvesterwork_kyt6tb5avngkrjjg2pfvzhocwaThu, 14 Jul 2022 00:00:00 GMTThe compact genetic algorithm struggles on Cliff functions
https://scholar.archive.org/work/6icnzz42h5gx7mqnxew3roh4ca
The compact genetic algorithm (cGA) is a non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the Cliff function for which it has been shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the Cliff function and investigate its dynamics both experimentally and theoretically around the Cliff. Our experimental results indicate that the cGA requires exponential time for all values of the update strength 𝐾. We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for 𝐾 where the expected optimization time drops from 𝑛 Θ(𝑛) to 2 Θ(𝑛) . CCS CONCEPTS • Theory of computation → Theory of randomized search heuristics.Frank Neumann, Dirk Sudholt, Carsten Wittwork_6icnzz42h5gx7mqnxew3roh4caFri, 08 Jul 2022 00:00:00 GMTZugang zu Informationen in digitalen Sammlungen: Fokus Archive
https://scholar.archive.org/work/bwex2rn7zncprglfnim3qntl7i
Zugang zu Informationen in digitalen Sammlungen: Fokus ArchiveTobias Hodel, Sonja Gasser, Christa Schneider, David Schochwork_bwex2rn7zncprglfnim3qntl7iWed, 22 Jun 2022 00:00:00 GMTThe antecedents and outcomes of export market orientation: A bibliometric review
https://scholar.archive.org/work/ozqshia7fjgvnicg7ggolxa4py
Export market orientation has always been a central issue in the study of international business and marketing. Therefore, it is essential to put effort into understanding the concept and its application in real-life business. Following in-depth studies and critical review papers in previous years, this research paper focuses on synthesizing and analyzing empirical studies in the period from 2015 to 2021, revolving around theoretical issues, context, the main features of the object, and the methodology. Within a predetermined time frame, twenty critical papers on export market orientation were selected and analyzed. The result reveals in detail that there are sixteen antecedents and five outcomes of export market orientation, as well as the total of eight moderators and seven mediators influencing their relationships. Findings have indicated that in recent years, models and theories of earlier eras have been developed and many of them have reached maturity.Phuong Ngoc Duy Nguyen, Tu Hoang Dinh, Anh Thao Van Dang, Tu Anh Tranwork_ozqshia7fjgvnicg7ggolxa4pyFri, 03 Jun 2022 00:00:00 GMTPercolation in Random Graphs of Unbounded Rank
https://scholar.archive.org/work/4dvyl3dhsjfqvbwebh755cw3ay
Bootstrap percolation in (random) graphs is a contagion dynamics among a set of vertices with certain threshold levels. The process is started by a set of initially infected vertices, and an initially uninfected vertex with threshold k gets infected once the number of its infected neighbors exceeds k. This process has been studied extensively in so called rank one models. These models can generate random graphs with heavy tailed degree sequence but they are not capable of clustering. In this paper we treat a class of random graphs of unbounded rank which allow for extensive clustering. Our main result determines the final fraction of infected vertices as the fixed point of a non-linear operator defined on a suitable function space. We propose an algorithm that facilitates neural networks to calculate this fixed point efficiently. We further derive criteria based on the Fréchet derivative of the operator that allows one to determine whether small infections spread through the entire graph or rather stay local.Nils Detering, Jimin Linwork_4dvyl3dhsjfqvbwebh755cw3aySun, 29 May 2022 00:00:00 GMTInvasion Dynamics in the Biased Voter Process
https://scholar.archive.org/work/htagrg5y7zg2bhfj3hkpv5ujcm
The voter process is a classic stochastic process that models the invasion of a mutant trait A (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait B, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias r∈(0,∞) quantifies the stochastic preference towards (r>1) or against (r<1) adopting A over B. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait A. In this paper we study the problem of fixation probability maximization under this model: given a budget k, find a set of k agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both r>1 and r<1, while the latter case is also inapproximable within any multiplicative factor. On the positive side, we show that when r>1, the optimization function is submodular and thus can be greedily approximated within a factor 1-1/e. An experimental evaluation of some proposed heuristics corroborates our results.Loke Durocher, Panagiotis Karras, Andreas Pavlogiannis, Josef Tkadlecwork_htagrg5y7zg2bhfj3hkpv5ujcmMon, 02 May 2022 00:00:00 GMTThe Compact Genetic Algorithm Struggles on Cliff Functions
https://scholar.archive.org/work/qxcggh7uxff2vjro72fbzat3um
The compact genetic algorithm (cGA) is an non-elitist estimation of distribution algorithm which has shown to be able to deal with difficult multimodal fitness landscapes that are hard to solve by elitist algorithms. In this paper, we investigate the cGA on the CLIFF function for which it has been shown recently that non-elitist evolutionary algorithms and artificial immune systems optimize it in expected polynomial time. We point out that the cGA faces major difficulties when solving the CLIFF function and investigate its dynamics both experimentally and theoretically around the cliff. Our experimental results indicate that the cGA requires exponential time for all values of the update strength K. We show theoretically that, under sensible assumptions, there is a negative drift when sampling around the location of the cliff. Experiments further suggest that there is a phase transition for K where the expected optimization time drops from n^Θ(n) to 2^Θ(n).Frank Neumann, Dirk Sudholt, Carsten Wittwork_qxcggh7uxff2vjro72fbzat3umMon, 11 Apr 2022 00:00:00 GMTSelf-adjusting Population Sizes for the (1, λ)-EA on Monotone Functions
https://scholar.archive.org/work/2epbsuonmbem7daei3ws54emly
We study the (1,λ)-EA with mutation rate c/n for c≤ 1, where the population size is adaptively controlled with the (1:s+1)-success rule. Recently, Hevia Fajardo and Sudholt have shown that this setup with c=1 is efficient on for s<1, but inefficient if s ≥ 18. Surprisingly, the hardest part is not close to the optimum, but rather at linear distance. We show that this behavior is not specific to . If s is small, then the algorithm is efficient on all monotone functions, and if s is large, then it needs superpolynomial time on all monotone functions. In the former case, for c<1 we show a O(n) upper bound for the number of generations and O(nlog n) for the number of function evaluations, and for c=1 we show O(nlog n) generations and O(n^2loglog n) evaluations. We also show formally that optimization is always fast, regardless of s, if the algorithm starts in proximity of the optimum. All results also hold in a dynamic environment where the fitness function changes in each generation.Marc Kaufmann, Maxime Larcher, Johannes Lengler, Xun Zouwork_2epbsuonmbem7daei3ws54emlyFri, 01 Apr 2022 00:00:00 GMTTwo-Dimensional Drift Analysis: Optimizing Two Functions Simultaneously Can Be Hard
https://scholar.archive.org/work/eptnkbjsvzespn42aqcgdmnoxi
In this paper we show how to use drift analysis in the case of two random variables X_1, X_2, when the drift is approximatively given by A· (X_1,X_2)^T for a matrix A. The non-trivial case is that X_1 and X_2 impede each other's progress, and we give a full characterization of this case. As application, we develop and analyze a minimal example TwoLinear of a dynamic environment that can be hard. The environment consists of two linear function f_1 and f_2 with positive weights 1 and n, and in each generation selection is based on one of them at random. They only differ in the set of positions that have weight 1 and n. We show that the (1+1)-EA with mutation rate χ/n is efficient for small χ on TwoLinear, but does not find the shared optimum in polynomial time for large χ.Duri Janett, Johannes Lenglerwork_eptnkbjsvzespn42aqcgdmnoxiMon, 28 Mar 2022 00:00:00 GMTGlobal Linear Convergence of Evolution Strategies on More Than Smooth Strongly Convex Functions
https://scholar.archive.org/work/pinkygymvvf6bbeb3cng4clsre
Evolution strategies (ESs) are zeroth-order stochastic black-box optimization heuristics invariant to monotonic transformations of the objective function. They evolve a multivariate normal distribution, from which candidate solutions are generated. Among different variants, CMA-ES is nowadays recognized as one of the state-of-the-art zeroth-order optimizers for difficult problems. Albeit ample empirical evidence that ESs with a step-size control mechanism converge linearly, theoretical guarantees of linear convergence of ESs have been established only on limited classes of functions. In particular, theoretical results on convex functions are missing, where zeroth-order and also first-order optimization methods are often analyzed. In this paper, we establish almost sure linear convergence and a bound on the expected hitting time of an ES family, namely the (1+1)_κ-ES, which includes the (1+1)-ES with (generalized) one-fifth success rule and an abstract covariance matrix adaptation with bounded condition number, on a broad class of functions. The analysis holds for monotonic transformations of positively homogeneous functions and of quadratically bounded functions, the latter of which particularly includes monotonic transformation of strongly convex functions with Lipschitz continuous gradient. As far as the authors know, this is the first work that proves linear convergence of ES on such a broad class of functions.Youhei Akimoto, Anne Auger, Tobias Glasmachers, Daiki Morinagawork_pinkygymvvf6bbeb3cng4clsreTue, 08 Feb 2022 00:00:00 GMTZugang zu Informationen in digitalen Sammlungen: Fokus Archive
https://scholar.archive.org/work/w2rohko4lrhzpe75vy4omdsx7u
Alle abgebildeten Beispiele in den Tabellen und zusätzliche Diagramme sind zusammen mit dem Code in der D3 Gallery zu finden, https://observablehq.com/@d3/gallery Informationswissenschaft: Theorie, Methode und Praxis, Bd. 7 (2022) ⎯-http://dx.doi.org/10.18755/iw.2022.5 Dieser Artikel ist lizenziert unter einer Creative Commons Namensnennung 4.0 International LizenzTobias Hodel, Sonja Gasser, Christa Schneider, David Schochwork_w2rohko4lrhzpe75vy4omdsx7uInvasion Dynamics in the Biased Voter Process
https://scholar.archive.org/work/b6goucrav5gkhltjjpb3orszge
The voter process is a classic stochastic process that models the invasion of a mutant trait A (e.g., a new opinion, belief, legend, genetic mutation, magnetic spin) in a population of agents (e.g., people, genes, particles) who share a resident trait B, spread over the nodes of a graph. An agent may adopt the trait of one of its neighbors at any time, while the invasion bias r quantifies the stochastic preference towards (r>1) or against (r<1) adopting A over B. Success is measured in terms of the fixation probability, i.e., the probability that eventually all agents have adopted the mutant trait A. In this paper we study the problem of fixation probability maximization under this model: given a budget k, find a set of k agents to initiate the invasion that maximizes the fixation probability. We show that the problem is NP-hard for both regimes r>1 and r<1, while the latter case is also inapproximable within any multiplicative factor that is independent of r. On the positive side, we show that when r>1, the optimization function is submodular and thus can be greedily approximated within a factor 1-1/e. An experimental evaluation of some proposed heuristics corroborates our results.Loke Durocher, Panagiotis Karras, Andreas Pavlogiannis, Josef Tkadlecwork_b6goucrav5gkhltjjpb3orszgeInvestigation of Infographic Design and Summarizing Processes from the Biology Texts of Primary School Students in Terms of Different Variables
https://scholar.archive.org/work/gytwrmbdnbadbk7po6x4a4oh2e
Technological developments have increased the importance of presenting and the processing of information as effective and memorable in the field of education. The aim of the study is to examine the effect of performing biology subjects in the elementary school science lessons with infographic design tasks in terms of the effects on students' cognitive structures and knowledge levels in the learning - teaching process. As the research method was used the pretest-posttest control group design from the experimental research models. The sample of the research consists of 4th grade students of a primary school. The total of 48 students were studied from two different classes, each of 24 students including in the study. Word Association Test (WAT) and achievement Test were used as data collection tools. Mann Whitney U test and Wilcoxon Signed Ranks test from non-parametric tests were used in the analysis of the data. As a result of the analyzes, it was seen that the students who carried out the infographic design activities were positively affected in terms of their cognitive structures.edanur inciwork_gytwrmbdnbadbk7po6x4a4oh2eFri, 31 Dec 2021 00:00:00 GMTSelf-Adjusting Mutation Rates with Provably Optimal Success Rules
https://scholar.archive.org/work/ed5qsnsgqnffbi675t24nhlaqi
The one-fifth success rule is one of the best-known and most widely accepted techniques to control the parameters of evolutionary algorithms. While it is often applied in the literal sense, a common interpretation sees the one-fifth success rule as a family of success-based updated rules that are determined by an update strength F and a success rate. We analyze in this work how the performance of the (1+1) Evolutionary Algorithm on LeadingOnes depends on these two hyper-parameters. Our main result shows that the best performance is obtained for small update strengths F=1+o(1) and success rate 1/e. We also prove that the running time obtained by this parameter setting is, apart from lower order terms, the same that is achieved with the best fitness-dependent mutation rate. We show similar results for the resampling variant of the (1+1) Evolutionary Algorithm, which enforces to flip at least one bit per iteration.Benjamin Doerr, Carola Doerr, Johannes Lenglerwork_ed5qsnsgqnffbi675t24nhlaqiTue, 28 Dec 2021 00:00:00 GMT