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.

Growth, migration and unemployment across Swedish municipalities

Fundamental questions in economics are why some regions are richer than others, why their economic growth rates vary, whether their growth tends to converge and the key factors that contribute to the variations. These questions have not yet been fully addressed, but changes in the local tax base are clearly influenced by the average income growth rate, net migration rate, and changes in unemployment rates. Thus, the main aim of this paper is to explore in depth the interactive effects of these factors (and local policy variables) in Swedish municipalities, by estimating a proposed three-equation system. Our main finding is that increases in local public expenditures and income taxes have negative effects on subsequent local income growth. In addition, our results support the conditional convergence hypothesis, i.e. that average income tends to grow more rapidly in relatively poor local jurisdictions than in initially “richer” jurisdictions, conditional on the other explanatory variables.

Informal sanctions and conditional cooperation: : A natural experiment on voluntary contributions to a public good.

In a natural experiment, this paper studies the impact of an informal sanctioning mechanism on individuals’ voluntary contribution to a public good. Cross-country skiers’ actual cash contributions in two ski resorts, one with and one without an informal sanctioning system, are used. I find the contributing share to be higher in the informal sanctioning system (79 percent) than in the non-sanctioning system (36 percent). Previous studies in one-shot public good situations have found an increasing conditional contribution (CC) function, i.e. the relationship between expected average contributions of other group members and the individual’s own contribution. In contrast, the present results suggest that the CC-function in the non-sanctioning system is non-increasing at high perceived levels of others’ contribution. This relationship deserves further testing in lab.

Fitting conditional and simultaneous autoregressive spatial models in hglm

We present a new version (> 2.0) of the hglm package for fitting hierarchical generalized linear models (HGLMs) with spatially correlated random effects. CAR() and SAR() families for conditional and simultaneous autoregressive random effects were implemented. Eigen decomposition of the matrix describing the spatial structure (e.g., the neighborhood matrix) was used to transform the CAR/SAR random effects into an independent, but eteroscedastic, Gaussian random effect. A linear predictor is fitted for the random effect variance to estimate the parameters in the CAR and SAR models. This gives a computationally efficient algorithm for moderately sized problems.