Parallel Evolutionary Computations [electronic resource] / edited by Nadia Nedjah, Luiza de Macedo Mourelle, Enrique Alba.Material type: TextLanguage: English Series: Studies in Computational Intelligence: 22Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2006Description: XXIII, 201 p. online resourceContent type: text Media type: computer Carrier type: online resourceISBN: 9783540328391Subject(s): Engineering | Artificial intelligence | Engineering mathematics | Engineering | Appl.Mathematics/Computational Methods of Engineering | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 519 LOC classification: TA329-348TA640-643Online resources: Click here to access online
Parallel Evolutionary Optimization -- A Model for Parallel Operators in Genetic Algorithms -- Parallel Evolutionary Multiobjective Optimization -- Parallel Hardware for Genetic Algorithms -- A Reconfigurable Parallel Hardware for Genetic Algorithms -- Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms -- Distributed Evolutionary Computation -- Performance of Distributed GAs on DNA Fragment Assembly -- On Parallel Evolutionary Algorithms on the Computational Grid -- Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit -- Parallel Particle Swarm Optimization -- Intelligent Parallel Particle Swarm Optimization Algorithms -- Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model.
"Parallel Evolutionary Computation" focuses on the aspects related to the parallelization of evolutionary computations, such as parallel genetic operators, parallel fitness evaluation, distributed genetic algorithms, and parallel hardware implementations, as well as on their impact on several applications. The book is divided into four parts. The first part deals with a clear software-like and algorithmic vision on parallel evolutionary optimizations. The second part is about hardware implementations of genetic algorithms, a valuable topic which is hard to find in the present literature. The third part treats the problem of distributed evolutionary computation and presents three interesting applications wherein parallel EC new ideas are featured. Finally, the last part deals with the up-to-date field of parallel particle swarm optimization to illustrate the intrinsic similarities and potential extensions to techniques in this domain. The book offers a wide spectrum of sample works developed in leading research throughout the world about parallel implementations of efficient techniques at the heart of computational intelligence. It will be useful both for beginners and experienced researchers in the field of computational intelligence.