High Performance Programming for Soft Computing

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High Performance Programming for Soft Computing

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High Performance Parallel Programming of a GA Using Multi-core Technology

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If for any reason your order is not available to ship, you will not be charged. Your order is also backed by our In-Stock Guarantee! What makes Biblio different? Facebook Instagram Twitter. Sign In Register Help Cart. As the underlying principles of soft computing are being understood, it is becoming clear that the ideas associated with this discipline are indeed powerful enough to solve computationally hard problems that are traditionally handled by conventional algorithmic non-soft techniques.

NP-hard problems arising in combinatorial optimization, such as those arising in DNA modeling, protein folding and drug design studies; ill-posed problems in the Hadamard sense such as the inversion of remotely sensed data, and texture segmentation and labelling in image processing; and equalization and detection of signals in a noisy environment such as those encountered in wireless communications are few examples of hard problems.

In computer science, soft computing methods have been used to optimize instruction and data pipelines in the central processing unit CPU , cell placement and routing in VLSI design, and optimal placement of files in heterogeneous distributed processing environments. In the arena of public policy, they have been used for multi-objective optimization in environmental pollution abatement and remediation.

Soft Computing Research Papers - neycacerposi.cf

Soft computing methods are particularly effective in search and optimization when the underlying search space is large, multimodal, and when the characteristics of the search space are not well understood. It is under these very conditions traditional optimization and search techniques such as steepest descent and dynamic programming become impractical. Artificial neural networks ANNs , which are essentially nonlinear mappers, use the brain metaphor.

They require hundreds of processors with thousands of interconnections. Indeed the original design of the Connection Machine was influenced by the "topology and architecture" of brains. Genetic Algorithms GAs are stochastic search and optimization techniques.

trifanslimar.tk GAs and evolutionary computation, which use the twin metaphors of natural evolution and survival of the fittest, derive their strength by working on populations of solutions and are therefore best exploited in a parallel computing environment. An off-shoot of GAs is genetic programming GP.

GAs and GPs function by iteratively refining a population of encoded representations of solutions or programs.

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