Ebook: Parallel Evolutionary Computations
- Tags: Appl.Mathematics/Computational Methods of Engineering, Artificial Intelligence (incl. Robotics)
- Series: Studies in Computational Intelligence 22
- Year: 2006
- Publisher: Springer-Verlag Berlin Heidelberg
- Edition: 1
- Language: English
- pdf
"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.
"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.
"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.
Content:
Front Matter....Pages I-XXIII
Front Matter....Pages I-XXIII
A Model for Parallel Operators in Genetic Algorithms....Pages 3-31
Parallel Evolutionary Multiobjective Optimization....Pages 33-56
Front Matter....Pages I-XXIII
A Reconfigurable Parallel Hardware for Genetic Algorithms....Pages 59-69
Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms....Pages 71-93
Front Matter....Pages I-XXIII
Performance of Distributed GAs on DNA Fragment Assembly....Pages 97-115
On Parallel Evolutionary Algorithms on the Computational Grid....Pages 117-132
Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit....Pages 133-155
Front Matter....Pages I-XXIII
Intelligent Parallel Particle Swarm Optimization Algorithms....Pages 159-175
Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model....Pages 177-198
Back Matter....Pages 199-201
"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.
Content:
Front Matter....Pages I-XXIII
Front Matter....Pages I-XXIII
A Model for Parallel Operators in Genetic Algorithms....Pages 3-31
Parallel Evolutionary Multiobjective Optimization....Pages 33-56
Front Matter....Pages I-XXIII
A Reconfigurable Parallel Hardware for Genetic Algorithms....Pages 59-69
Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms....Pages 71-93
Front Matter....Pages I-XXIII
Performance of Distributed GAs on DNA Fragment Assembly....Pages 97-115
On Parallel Evolutionary Algorithms on the Computational Grid....Pages 117-132
Parallel Evolutionary Algorithms on Consumer-Level Graphics Processing Unit....Pages 133-155
Front Matter....Pages I-XXIII
Intelligent Parallel Particle Swarm Optimization Algorithms....Pages 159-175
Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model....Pages 177-198
Back Matter....Pages 199-201
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