An Evaluation of”Middle Ages Dead or Live?”The first interactive exhibition at the National Museum of History

This is a study conducted at, and for, the National Museum of History in Stockholm. The aim of the study was to confirm or disconfirm the hypothesis that visitors in a traditional museum environment might not take part in interactivity in an interactive exhibition. And if they do the visitors might skip the texts and objects on display. To answer this and other questions a multiple method was used. Both non participant observations and exit interviews were conducted. After a description of the interactive exhibits, theory of knowledge and learning is presented before the gathered data is presented. All together 443 visitors were observed. In the observations the visitors were timed on how much time they spent in the room, the time spent on the interactivity, texts and objects. In the 40 interviews information about visitors’ participation in the interactivity was gathered. What interactivity the visitor found easiest, hardest, funniest and most boring.The result did not confirm the hypothesis. All kinds of visitors, children and adults, participated in the interactivities. The visitors took part in the texts and objects and the interactive exhibits.

Dynamic Fuzzy Logic Control of GeneticAlgorithm Probabilities

Genetic algorithms are commonly used to solve combinatorial optimizationproblems. The implementation evolves using genetic operators (crossover, mutation,selection, etc.). Anyway, genetic algorithms like some other methods have parameters(population size, probabilities of crossover and mutation) which need to be tune orchosen.In this paper, our project is based on an existing hybrid genetic algorithmworking on the multiprocessor scheduling problem. We propose a hybrid Fuzzy-Genetic Algorithm (FLGA) approach to solve the multiprocessor scheduling problem.The algorithm consists in adding a fuzzy logic controller to control and tunedynamically different parameters (probabilities of crossover and mutation), in anattempt to improve the algorithm performance. For this purpose, we will design afuzzy logic controller based on fuzzy rules to control the probabilities of crossoverand mutation. Compared with the Standard Genetic Algorithm (SGA), the resultsclearly demonstrate that the FLGA method performs significantly better.