1,720,962 research outputs found
Novel Approaches for Offline Data-Driven Evolutionary Multiobjective Optimization
No full textMost multiobjective evolutionary algorithms (MOEAs) assume that analytical functions or simulation models are available while solving a multiobjective optimization problem (MOP). However, in some cases we must start with data and build approximation models known as surrogates that are later used to solve the MOP by an MOEA. These types of problems are called data-driven MOPs. This
thesis is devoted to solving so-called offline data-driven MOPs that are particularly challenging as no new data is available during the optimization process.
The author first presents approaches to utilize the uncertainty in the prediction of Kriging or Gaussian process (GP) surrogates as additional objectives. However, these approaches increase the complexity of the MOP being solved. Hence, the author proposes probabilistic selection approaches that can be embedded in a decomposition-based MOEA without further analytical derivations.
These approaches utilize Monte Carlo sampling and kernel density estimation to calculate the probability of selection criterion of the MOEA and later select individuals based on them. Next, the author proposes an interactive optimization framework that utilizes decision maker’s preferences for uncertainties in addition to preferences for objective values. The framework was further extended to use probabilistic selection approaches for a decomposition-based MOEA and a custom reference vector adaptation technique to consider uncertainty in the solutions during the adaptation process.
Building GPs with all the provided data becomes computationally expensive when the size of the data is large. Hence, the author finally proposes treed GP surrogates for multiobjective optimization (TGP-MO). They can be built with a relatively low computational cost and have a good accuracy exclusively in the regions around the optimal solutions. This thesis provides multiple novel approaches
and detailed experimental studies for solving offline data-driven MOPs with decision support that will enhance real-world problem-solving capabilities.
Keywords: metamodelling, surrogates, Pareto optimality, Kriging, Gaussian processes, evolutionary algorithm, decision making, uncertainty, interactive methods, preference informationunknown accessibilityei tietoa saavutettavuudest
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Hybrid Surrogate Assisted Evolutionary Multiobjective Reinforcement Learning for Continuous Robot Control
No full textPublisher Copyright: © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.Many real world reinforcement learning (RL) problems consist of multiple conflicting objective functions that need to be optimized simultaneously. Finding these optimal policies (known as Pareto optimal policies) for different preferences of objectives requires extensive state space exploration. Thus, obtaining a dense set of Pareto optimal policies is challenging and often reduces the sample efficiency. In this paper, we propose a hybrid multiobjective policy optimization approach for solving multiobjective reinforcement learning (MORL) problems with continuous actions. Our approach combines the faster convergence of multiobjective policy gradient (MOPG) and a surrogate assisted multiobjective evolutionary algorithm (MOEA) to produce a dense set of Pareto optimal policies. The solutions found by the MOPG algorithm are utilized to build computationally inexpensive surrogate models in the parameter space of the policies that approximate the return of policies. An MOEA is executed that utilizes the surrogates’ mean prediction and uncertainty in the prediction to find approximate optimal policies. The final solution policies are later evaluated using the simulator and stored in an archive. Tests on multiobjective continuous action RL benchmarks show that a hybrid surrogate assisted multiobjective evolutionary optimizer with robust selection criterion produces a dense set of Pareto optimal policies without extensively exploring the state space. We also apply the proposed approach to train Pareto optimal agents for autonomous driving, where the hybrid approach produced superior results compared to a state-of-the-art MOPG algorithm.Peer reviewe
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
koamabayili/VECTRON-author-checklist: VECTRON author checklist
No full textWe have done our best to complete the author checklist relating to the use of animals in the hut study. Note that the objective for the hut study was to evaluate the IRS treatment applications for residual efficacy against Anopheles mosquitoes, including the local An. coluzzii mosquito population. Cows were only used to attract mosquitoes into the huts and no tests were carried out directly on the cows. The author checklist is intended for use with studies where experiments are carried out on animals, which is why we have had such difficulty in completing this for the hut study, as many of the questions do not relate to how the cows were used
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