Externally indexed torrent

If you are the original uploader, contact staff to have it moved to your account

Textbook in PDF format

The recent book of the series continues the collection of articles dealing with the important and efficient combination of traditional and novel mathematical approaches with various computational intelligence techniques, with a stress of fuzzy systems, and fuzzy logic.
Complex problems and systems, which prevail in the real world, cannot often be tackled and solved either by traditional methods offered by mathematics or even the traditional Computer Science (CS) and Artificial Intelligence (AI). What is the way out of this dilemma? Advanced methodologies, and tools and techniques, "mimicking" human reasoning or the behavior of animals, animal populations or certain parts of the living bod, based on traditional Computer Science and the initial approaches of Artificial Intelligence are often referred to as biologically inspired methods, or often Computational Intelligence (CI). Computational Intelligence offers effective and efficient solutions to many "unsolvable" problems. However, it is far from being a ready to use and complete collection of approaches, and is rather a continuously developing field without clear borders. The emerging new models and algorithms of Computational Intelligence are deeply rooted in the vast apparatus of traditional mathematics.
The chapter "Fuzzy Logic Programming with Generalized Quantifiers" introduces a new definition of the immediate consequences operator considered in many fuzzy logic programming frameworks, focused on weakening the existential feature of the supremum operator. This new definition has been possible taking advantage of the notion of generalized quantifiers, which provides weaker quantifiers than the existential one.
In the chapter "Experiments with the Discrete Bacterial Memetic Evolutionary Algorithm for Solving the Cumulative Capacitated Vehicle Routing Problem" we present our initial experiments with the Discrete Bacterial Memetic Evolutionary Algorithm for solving the Cumulative Capacitated Vehicle Routing Problem. The algorithm was tested on instances proposed in the literature. However our method was able to find the optimal solution for small (around 50 nodes) instances, but its convergence speed is low. In the last section some of our ideas to improve the performance of the algorithm were presented