Nedjah N., Alba E., de Macedo Mourelle L. (eds.) Parallel Evolutionary Computations

Springer, 2006. — 213 p.Evolutionary algorithms and their related computations are solver systems which use computational models inspired in Darwinian natural selection processes as a key element in their design and implementation. In general, evolutionary computation (EC) is used to solve NP-hard problems which cannot be solved with other tools because of their intrinsic difficulty, high dimensionality or incomplete definition. In practice, EC is composed of a set of different families of algorithms that iteratively improve a set of tentative solutions to obtain an optimal or quasi-optimal solution to the problem. Therefore, evolutionary algorithms require sometimes a massive computational effort to yield efficient and competitive solutions to real-size engineering problems which would otherwise rest unsolved today.
This book 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 the impact of parallel EC 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 goal of this volume has been to offer 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. The book should be useful both for beginners and experienced researchers in the field of computational intelligence.Part I Parallel Evolutionary Optimization.
A Model for Parallel Operators in Genetic Algorithms.
Parallel Evolutionary Multiobjective Optimization.
Part II Parallel Hardware for Genetic Algorithms.
A Reconfigurable Parallel Hardware for Genetic Algorithms.
Reconfigurable Computing and Parallelism for Implementing and Accelerating Evolutionary Algorithms.
Part III 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.
Part IV Parallel Particle Swarm Optimization.
Intelligent Parallel Particle Swarm Optimization Algorithms.
Parallel Ant Colony Optimization for 3D Protein Structure Prediction using the HP Lattice Model.