Spatially Structured Evolutionary Algorithms Artificial Evolution in Space and Time /

Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured dispos...

Πλήρης περιγραφή

Λεπτομέρειες βιβλιογραφικής εγγραφής
Κύριος συγγραφέας: Tomassini, Marco (Συγγραφέας)
Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
Γλώσσα:English
Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2005.
Σειρά:Natural Computing Series,
Θέματα:
Διαθέσιμο Online:Full Text via HEAL-Link
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100 1 |a Tomassini, Marco.  |e author. 
245 1 0 |a Spatially Structured Evolutionary Algorithms  |h [electronic resource] :  |b Artificial Evolution in Space and Time /  |c by Marco Tomassini. 
264 1 |a Berlin, Heidelberg :  |b Springer Berlin Heidelberg,  |c 2005. 
300 |a XIII, 193 p.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Natural Computing Series,  |x 1619-7127 
505 0 |a Setting the Stage for Structured Populations -- Island Models -- Island Models: Empirical Properties -- Lattice Cellular Models -- Lattice Cellular Models: Empirical Properties -- Random and Irregular Cellular Populations -- Coevolutionary Structured Models -- Some Nonconventional Models. 
520 |a Evolutionary algorithms (EAs) is now a mature problem-solving family of heuristics that has found its way into many important real-life problems and into leading-edge scientific research. Spatially structured EAs have different properties than standard, mixing EAs. By virtue of the structured disposition of the population members they bring about 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. Moreover, there is new material on non-standard networked population structures such as small-world networks. The book should be of interest to advanced undergraduate and graduate students working in evolutionary computation, machine learning, and optimization. It should also be useful to researchers and professionals working in fields where the topological structures of populations and their evolution plays a role. 
650 0 |a Computer science. 
650 0 |a Computer programming. 
650 0 |a Computers. 
650 0 |a Algorithms. 
650 0 |a Numerical analysis. 
650 0 |a Artificial intelligence. 
650 1 4 |a Computer Science. 
650 2 4 |a Programming Techniques. 
650 2 4 |a Artificial Intelligence (incl. Robotics). 
650 2 4 |a Theory of Computation. 
650 2 4 |a Computation by Abstract Devices. 
650 2 4 |a Algorithm Analysis and Problem Complexity. 
650 2 4 |a Numeric Computing. 
710 2 |a SpringerLink (Online service) 
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776 0 8 |i Printed edition:  |z 9783540241935 
830 0 |a Natural Computing Series,  |x 1619-7127 
856 4 0 |u http://dx.doi.org/10.1007/3-540-29938-6  |z Full Text via HEAL-Link 
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950 |a Computer Science (Springer-11645)