Searching the solution space in constructive geometric constraint solving with genetic algorithms

Geometric problems defined by constraints have an exponential number of solution instances in the number of geometric elements involved. Generally, the user is only interested in one instance such that besides fulfilling the geometric constraints, exhibits some additional properties. Selecting a solution instance amounts to selecting a given root every time the geometric constraint solver needs to compute the zeros of a multi valuated function. The problem of selecting a given root is known as the Root Identification Problem. In this paper we present a new technique to solve the root identification problem. The technique is based on an automatic search in the space of solutions performed by a genetic algorithm. The user specifies the solution of interest by defining a set of additional constraints on the geometric elements which drive the search of the genetic algorithm. The method is extended with a sequential niche technique to compute multiple solutions. A number of case studies illustrate the performance of the method.Postprint (published version

How group identification distorts beliefs

This paper investigates how group identification distorts people’s beliefs about the ability of their peers in social groups. We find that experimentally manipulated identification with a randomly composed group leads to overconfident beliefs about fellow group members’ performance on an intelligence test. This result cannot be explained by individual overconfidence, i.e., participants overconfident in their own skill believing that their group performed better because of them, as this was ruled out by experimental design. Moreover, we find that participants with stronger group identification put more weight on positive signals about their group when updating their beliefs. These in-group biases in beliefs can have important economic consequences when group membership is used to make inference about an individual’s characteristics as, for instance, in hiring decisions

Metamodel Instance Generation: A systematic literature review

Modelling and thus metamodelling have become increasingly important in Software Engineering through the use of Model Driven Engineering. In this paper we present a systematic literature review of instance generation techniques for metamodels, i.e. the process of automatically generating models from a given metamodel. We start by presenting a set of research questions that our review is intended to answer. We then identify the main topics that are related to metamodel instance generation techniques, and use these to initiate our literature search. This search resulted in the identification of 34 key papers in the area, and each of these is reviewed here and discussed in detail. The outcome is that we are able to identify a knowledge gap in this field, and we offer suggestions as to some potential directions for future research.Comment: 25 page

Inter-database instance identification in composite information systems

Bibliography: p. 20-21.Y. Richard Wang, Stuart E. Madnick, David C. Horton