1,720,997 research outputs found
Community of scientist optimization: An autonomy oriented approach to distributed optimization
No full textA novel optimization paradigm, Community of Scientists Optimization (CoSO), is presented in this
paper. The approach is inspired to the behaviour of a community of scientists interacting, pursuing for research results and foraging the funds needed to held their research activities. The CoSO metaphor can be applied to general optimization domains, where optimal solutions emerge from the collective behaviour of a distributed community of interacting autonomous entities.
The CoSO framework presents analogies and remarkable differences with other evolutionary optimization
approaches: swarm behaviour, foraging and selectionmechanism based on research funds competition, dynamically evolving multicapacity communication channels realized by journals and evolving population size regulated by research management strategies.
Experiments and comparisons on benchmark problems show the effectiveness of the approach for numerical
optimization. CoSO, with the design of appropriate foraging and competition strategies, also represents a great potential as a general meta-heuristic for applications in non-numerical and agent-based domains.A novel optimization paradigm, called Community of Scientists Optimization (CoSO), is presented in this paper. The approach is inspired to the behaviour of a community of scientists interacting, pursuing for research results and foraging the funds needed to held their research activities. The CoSO metaphor can be applied to general optimization domains, where optimal solutions emerge from the collective behaviour of a distributed community of interacting autonomous entities. The CoSO framework presents analogies and remarkable differences with other evolutionary optimization approaches: swarm behaviour, foraging and selection mechanism based on research funds competition, dynamically evolving multicapacity communication channels realized by journals and evolving population size regulated by research management strategies. Experiments and comparisons on benchmark problems show the effectiveness of the approach for numerical optimization. CoSO, with the design of appropriate foraging and competition strategies, also represents a great potential as a general meta-heuristic for applications in non-numerical and agent-based domains. © 2012 - IOS Press and the authors. All rights reserved
Modeling the dynamics of energy commodity prices using neural networks
No full textThe deregulation of energy commodity markets has caused changes in the dynamics of commodity prices.
One main aim of deregulation is to allow markets to respond to supply and demand conditions causing
more competitive markets environments.
Energy commodities have recently become an asset class used as an investment tool by various kind of
market participants. Spot markets for oil, gas and electricity are at hand and are largely used also by
financial institutions besides the traditional retailers or producers. In addition, a large set of energy
derivatives are currently traded in most European and US Exchanges providing an useful tool to hedge risk. We use time series of daily prices of oil, gas and electricity for the period 2001-2010. Time series have
been arranged in 3-years slots, since all the predictors have been trained on a 2-years time window and
tested on the successive 1-year period
An optimisation-driven prediction method for automated diagnosis and prognosis
No full textThis article presents a novel hybrid classification paradigm for medical diagnoses and prognoses prediction. The core mechanism of the proposed method relies on a centroid classification algorithm whose logic is exploited to formulate the classification task as a real-valued optimisation problem. A novel metaheuristic combining the algorithmic structure of Swarm Intelligence optimisers with the probabilistic search models of Estimation of Distribution Algorithms is designed to optimise such a problem, thus leading to high-accuracy predictions. This method is tested over 11 medical datasets and compared against 14 cherry-picked classification algorithms. Results show that the proposed approach is competitive and superior to the state-of-the-art on several occasions
Optimization through Iterative Smooth Morphological Transformations
No full textIn this paper, we introduce SMorph, a new methodology for combinatorial optimization that works in the instance space of the problem at hand. Indeed, given the problem instance to solve, SMorph builds a simplified instance whose optimum is easy to locate, then it iteratively evolves this instance towards the target one by alternating two steps: optimization and smooth transformation of the current instance. The knowledge acquired in each iteration is transferred to next one, while the entire process is designed with the aim of improving the last optimization step. Although the abstract search scheme of SMorph is general enough to be instantiated for a variety of combinatorial optimization problems, here we present an implementation for the well-known Linear Optimization Problem (LOP). Experiments have been conducted on a set of commonly adopted benchmark instances of the LOP, and the results validate the proposed approach
Variable neighborhood algebraic Differential Evolution: An application to the Linear Ordering Problem with Cumulative Costs
No full textAlgebraic variants of the Differential Evolution (DE) algorithm have been recently proposed to tackle permutation-based optimization problems by means of an algebraic framework, which allows to directly encode the solutions as permutations. The algebraic DE in the permutation space can be characterized by considering different neighborhood definitions such as swapping two adjacent items, swapping any two items, shifting an item to a given position. Here we propose the Variable Neighborhood Differential Evolution for Permutations (VNDEP), which adaptively searches the three neighborhoods together based on a method of dynamic reward. We provide an extensive and systematic analysis of the theoretical tools required in VNDEP, by studying the complexity of the proposed algorithmic components and by introducing the possibility to use a scale factor parameter larger than one. Experiments have been held on a widely used benchmark suite for the Linear Ordering Problem with Cumulative Costs, where VNDEP has been compared with four known permutation-based DE schemes and with respect to the state-of-the-art results for the considered instances. The experiments clearly show that VNDEP systematically outperforms the competitor algorithms and, most impressively, 32 new best known solutions, of the 50 most challenging instances, have been obtained
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
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
Classification of Text Writing Proficiency of L2 Learners
No full textIn this study, we present a novel system for the automatic classification of text complexity in the Italian language, focusing on the phraseological dimension. This quantitative assessment of text complexity is crucial for various applications, including text readability measurement, text simplification, and support for educators during evaluation processes. We use a dataset comprising texts written by Italian L2 learners and classified according to the levels of the Common European Framework of Reference for Languages. The dataset texts serve as a basis for calculating phraseological features, which are then used as input for multiple machine-learning classifiers to compare their performance in predicting proficiency levels. Our experimental results demonstrate that the proposed framework effectively harnesses phraseological complexity features to achieve high classification accuracy in determining proficiency levels
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