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Evolutionary algorithms (EAs) are accepted as a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. The unique properties of spatially structured EAs evoke new dynamical features that can be harnessed to solve difficult problems faster and more efficiently. This book describes the state of the art in spatially structured EAs by using graph concepts as a unifying theme. The models, their analysis, and their empirical behavior are presented in detail. Included is new material on non-standard networked population structures such as small-world networks. The book will be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization, and also to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role.
Soft computing encompasses various computational methodologies, which, unlike conventional algorithms, are tolerant of imprecision, uncertainty, and partial truth. Soft computing technologies offer adaptability as a characteristic feature and thus permit the tracking of a problem through a changing environment. Besides some recent developments in areas like rough sets and probabilistic networks, fuzzy logic, evolutionary algorithms, and artificial neural networks are core ingredients of soft computing, which are all bio-inspired and can easily be combined synergetically.This book presents a well-balanced integration of fuzzy logic, evolutionary computing, and neural information processing. The three constituents are introduced to the reader systematically and brought together in differentiated combinations step by step. The text was developed from courses given by the authors and offers numerous illustrations as
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