本文对国际科学计量学杂志《Ｓｃｉｅｎｔｏｍｅｔｒｉｃｓ》１９７９－１９９１年的研究论文内容、栏目、作者及国别和编委及国别作了计量分析，揭示出科学计量学研究的重点、活动的中心及发展趋势，说明了学科带头人在发展科学计量学这门新兴学科中的作用。

国际刊物《Scientometrics》文献计量研究

Differential Evolution (DE) is arguably one of the most powerful and versatile evolutionary optimizers for the continuous parameter spaces in recent times. Almost 5 years have passed since the first comprehensive survey article was published on DE by Das and Suganthan in 2011. Several developments have been reported on various aspects of the algorithm in these 5 years and the research on and with DE have now reached an impressive state. Considering the huge progress of research with DE and its applications in diverse domains of science and technology, we find that it is a high time to provide a critical review of the latest literatures published and also to point out some important future avenues of research. The purpose of this paper is to summarize and organize the information on these current developments on DE. Beginning with a comprehensive foundation of the basic DE family of algorithms, we proceed through the recent proposals on parameter adaptation of DE, DE-based single-objective global optimizers, DE adopted for various optimization scenarios including constrained, large-scale, multi-objective, multi-modal and dynamic optimization, hybridization of DE with other optimizers, and also the multi-faceted literature on applications of DE. The paper also presents a dozen of interesting open problems and future research issues on DE.

Recent advances in differential evolution – An updated survey

This chapter is devoted to a more detailed examination of game theory. Game theory is an important tool for analyzing strategic behavior, is concerned with how individuals make decisions when they recognize that their actions affect, and are affected by, the actions of other individuals or groups. Strategic behavior recognizes that the decision-making process is frequently mutually interdependent. Game theory is the study of the strategic behavior involving the interaction of two or more individuals, teams, or firms, usually referred to as players. Two game theoretic scenarios were examined in this chapter: Simultaneous-move and multi-stage games. In simultaneous-move games the players effectively move at the same time. A normal-form game summarizes the players, possible strategies and payoffs from alternative strategies in a simultaneous-move game. Simultaneous-move games may be either noncooperative or cooperative. In contrast to noncooperative games, players of cooperative games engage in collusive behavior. A Nash equilibrium, which is a solution to a problem in game theory, occurs when the players’ payoffs cannot be improved by changing strategies. Simultaneous-move games may be either one-shot or repeated games. One-shot games are played only once. Repeated games are games that are played more than once. Infinitely-repeated games are played over and over again without end. Finitely-repeated games are played a limited number of times. Finitely-repeated games have certain or uncertain ends.

An Introduction to Game Theory

Two-person Cooperative Games

Market power and strategic interaction in electricity networks

Graphical abstractDisplay Omitted HighlightsMost existing approaches reduce the multi-objective optimization problem of QoS-aware web service composition to a single-objective problem using scalarization.No comparative study with several multi-objective algorithms has been found in the literature for this specific problem.Several EMOAs are tested: NSGA-II, SPEA2, POSDE, GDE3, and MOEA/D.Differential evolution (DE) algorithms - in particular GDE3 - yields the best results on this specific problem for several scenarios, also having the lowest time complexity. Web service composition combines available services to provide new functionality. The various available services have different quality-of-service (QoS) attributes. Building a QoS-optimal web service composition is a multi-criteria NP-hard problem. Most of the existing approaches reduce this problem to a single-criterion problem by aggregating different criteria into a unique global score (scalarization). However, scalarization has some significant drawbacks: the end user is supposed to have a complete a priori knowledge of its preferences/constraints about the desired solutions and there is no guarantee that the aggregated results match it. Moreover, non-convex parts of the Pareto set cannot be reached by optimizing a convex weighted sum. An alternative is to use Pareto-based approaches that enable a more accurate selection of the end-user solution. However, so far, only few solutions based on these approaches have been proposed and there exists no comparative study published to date. This motivated us to perform an analysis of several state-of-the-art multi-objective evolutionary algorithms. Multiple scenarios with different complexities are considered. Performance metrics are used to compare several evolutionary algorithms. Results indicate that GDE3 algorithm yields the best performances on this problem, also with the lowest time complexity.

Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition

Recent advances in brain imaging technology, coupled with large-scale brain research projects, such as the BRAIN initiative in the U.S. and the European Human Brain Project, allow us to capture brain activity in unprecedented details. In principle, the observed data is expected to substantially shape our knowledge about brain activity, which includes the development of new biomarkers of brain disorders. However, due to the high dimensionality, the analysis of the data is challenging, and selection of relevant features is one of the most important analytic tasks. In many cases, due to the complexity of search space, evolutionary algorithms are appropriate to solve the aforementioned task. In this chapter, we consider the feature selection task from the point of view of classification tasks related to functional magnetic resonance imaging (fMRI) data. Furthermore, we present an empirical comparison of conventional LASSO-based feature selection and a novel feature selection approach designed for fMRI data based on a simple genetic algorithm.

Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

Co-authorship networks have been used to study collaboration patterns in various fields, evaluate researchers and recommend policies. In their simplest form they are constructed by considering authors to be network nodes connected to each other if they published a paper together. We propose to further explore the same data by constructing a different network, in which nodes are articles linked to one another if they have a common author. For papers published in the fields of computer science and mathematics with affiliations to Romanian institutions, we show that this type of network reveals patterns of collaborative behavior and offers new insights about practices in the field. We find that the proposed networks are smaller and denser than the co-authorship networks, have a better defined community structure, and directly represent the results of collaborative endeavors by focusing on the actual outcome, i.e., published papers.

A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks

Representation and analysis of publication data in the form of a network has become a common method of illustrating and evaluating the scientific output of a group or of a scientific field.
 Co-authorship networks also reveal patterns and collaboration practices. In this paper we propose the use of a hypergraph model—a generalized network—to represent publication data by considering papers as hypergraph nodes. Hyperedges, connecting the nodes, represent the authors connecting all their papers. We show that this representation is more straightforward than other authorship network models.
 Using the hypergraph model we propose a collaboration measure of an author that reflects the influence of that author over the collaborations of its co-authors. We illustrate the introduced concepts by analyzing publishing data of computer scientists and mathematicians in Romania over a 10 year period.

A hypergraph model for representing scientific output

The aim of our research is to find an efficient solution to the services QoS optimization problem. This NP-hard problem is well known in the service-oriented computing field: given a business workflow that includes a set of abstract services and a set of concrete service implementations for each abstract service, the goal is to find the optimal combination of concrete services. The majority of recent proposals indicate the Genetic Algorithms (GA) as the best approach for complex workflows. But this problem usually needs to be solved at runtime, a task for which GA may be too slow. We propose a new approach, based on Differential Evolution (DE), that converges faster and it is more scalable and robust than the existing solutions based on Genetic Algorithms.

Mihai Alexandru Suciu

Papers

Comparative analysis of multi-objective evolutionary algorithms for QoS-aware web service composition

Feature Selection with a Genetic Algorithm for Classification of Brain Imaging Data

A new network model for the study of scientific collaborations: Romanian computer science and mathematics co-authorship networks

A hypergraph model for representing scientific output

QoS-based service optimization using differential evolution