Publishing Trends in Economics across Colleges and Universities, 1991-2007

There is good reason to think that non-elite programs in economics may be producing relatively more research than in the past: Research expectations have been ramped-up at non-PhD institutions and new information technologies have changed the way academic knowledge is produced and exchanged. This study investigates this question by examining publishing productivity in economics (and business) using data from the Web of Science (Knowledge) for a broad set of institutions – both elite and non-elite – over a 17-year period, from 1991 through 2007. Institutions are grouped into six tiers using a variety of sources. The analysis provides evidence that non-elite institutions are gaining on their more elite counterparts, but the magnitude of the gains are small. Thus, the story is more of constancy than of change, even in the face of changing technology and rising research expectations.higher education, research productivity, publishing trends, inequality

Optimization of coefficients of lists of polynomials by evolutionary algorithms

We here discuss the optimization of coefficients of lists of polynomials using evolutionary computation. The given polynomials have 5 variables, namely t, a1, a2, a3, a4, and integer coefficients. The goal is to find integer values i, with i 2 {1, 2, 3, 4}, substituting ai such that, after crossing out the gcd (greatest common divisor) of all coefficients of the polynomials, the resulting integers are minimized in absolute value. Evolution strategies, a special class of heuristic, evolutionary algorithms, are here used for solving this problem. In this paper we describe this approach in detail and analyze test results achieved for two benchmark problem instances; we also show a visual analysis of the fitness landscapes of these problem instancesThe authors thank Franz Winkler at the Research Institute for Symbolic Computation, Johannes Kepler University Linz, for his advice. R. Sendra is partially supported by the Spanish Ministerio de Economía y Competitividad under the project MTM2011-25816-C02-01 and is a member of the Research Group ASYNACS (Ref. CCEE2011/R34). The authors also thanks members of the Heuristic and Evolutionary Algorithms Laboratory as well as of the Bioinformatics Research Group, University of Applied Sciences Upper Austria, for their comments

Optimization of coefficients of lists of polynomials by evolutionary algorithms

We here discuss the optimization of coefficients of lists of polynomials using evolutionary computation. The given polynomials have 5 variables, namely t, a1, a2, a3, a4, and integer coefficients. The goal is to find integer values i, with i 2 {1, 2, 3, 4}, substituting ai such that, after crossing out the gcd (greatest common divisor) of all coefficients of the polynomials, the resulting integers are minimized in absolute value. Evolution strategies, a special class of heuristic, evolutionary algorithms, are here used for solving this problem. In this paper we describe this approach in detail and analyze test results achieved for two benchmark problem instances; we also show a visual analysis of the fitness landscapes of these problem instancesMinisterio de Ciencia e Innovació

Fitness landscape analysis in the optimization of coefficients of curve parametrizations

Este documento se considera que es una ponencia de congresos en lugar de un capítulo de libro.Computer Aided Systems Theory - EUROCAST 2017, 19-24 February, Las Palmas de Gran Canaria, Spain.J.R. Sendra is member of the Research Group ASYNACS (Ref.CT-CE2019/683)Parametric representations of geometric objects, such as curves or surfaces, may have unnecessarily huge integer coefficients. Our goal is to search for an alternative parametric representation of the same object with significantly smaller integer coefficients. We have developed and implemented an evolutionary algorithm that is able to find solutions to this problem in an efficient as well as robust way. In this paper we analyze the fitness landscapes associated with this evolutionary algorithm. We here discuss the use of three different strategies that are used to evaluate and order partial solutions. These orderings lead to different landscapes of combinations of partial solutions in which the optimal solutions are searched. We see that the choice of this ordering strategy has a huge inuence on the characteristics of the resulting landscapes, which are in this paper analyzed using a set of metrics, and also on the quality of the solutions that can be found by the subsequent evolutionary search.Ministerio de Economía y CompetitividadEuropean Regional Development FundAustrian Research Promotion Agenc

Publishing Trends in Economics across Colleges and Universities, 1991-2007

Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may b