Titlebook: Soft Computing: Methodologies and Applications; Frank Hoffmann,Mario Köppen,Rajkumar Roy Conference proceedings 2005 Springer-Verlag Berli

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Soft Computing Applications in Traffic and Transport Systems: A Reviewo their ability to handle quantitative and qualitative measures, and to efficiently solve problems which involve complexity, imprecision and uncertainty. This paper provides a survey of soft computing applications. A classification scheme for soft computing applications is defined. The current frame

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Audiometry

Clustering of Activity Patterns Using Genetic Algorithmsortant issue in models of activity-based approaches to travel demand analysis. This knowledge is critical to many activity-based models, and it aids our understanding of activity/travel behavior. This paper aims to develop a methodology for the clustering of these patterns. There is a large number o

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A Mathematical Model for Evaluation of Information Effects in ATIS (Advanced Traveler Information Syis achieved through information, consequentially information itself is a crucial issue in transportation problems. If information was perfect, users could easily choose the best path from their point of view, but unfortunately complete and precise information about network conditions is rarely avail

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An ALife-Inspired Evolutionary Algorithm for Dynamic Multiobjective Optimization Problemsime. Several studies are available in the literature about the use of genetic algorithms for time dependent fitness landscape in single-objective optimization problems. But when dynamic multi-objective optimization is concerned, very few studies can be found. Taking inspiration from Artificial Life

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Optimum Tests Selection for Analog Circuits with the Use of Genetic Algorithm are taken into account by the presented algorithm. Genetic algorithms are used as a new method for optimum test selection. This approach enhances quality and speeds up finding suboptimal solutions by finding more then one good result in each genetic algorithm cycle. Results for a hypothetical examp

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Unsupervised Pixel Clustering in Multispectral Images by Genetic Programmingis faced in terms of unsupervised pixel classification. Given an image consisting in . bands, the goal is to find the optimal number of clusters and the positions of their centres in the .-dimensional hyperspace, which allow the best possible description of the image. The pixels are then assigned to