Abstract: The Graph Partitioning Problem (GPP) has several practical applications in many areas, such as design of VLSI ( Very-large-scale integration) circuits, solution of numerical methods for simulation problems that include factorization of sparse matrix and partitioning of finite elements meshes for parallel programming applications, between others. The GPP tends to be NP-hard and optimal solutions for solving them are infeasible when the number of vertices of the graph is very large. There has been an increased used of heuristic and metaheuristic algorithms to solve the PPG to get good results where exceptional results are not obtainable by practical means. This article proposes an efficient parallel solution to the GPP problem based on the implementation of existing heuristics in a computational cluster. The proposed solution improves the execution time and, by introducing some random features into the original heuristics, improve the quality of the created partitions.

• Publication Date: 05-Jan-2014
• DOI: 10.15224/978-981-07-8859-9-06
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Abstract: Many problems of the real world vary with number of parameters affecting the system and their computations become a difficult task. Artificial neural networks (ANN) can learn and be trained on a set of input and output data belonging to a particular problem. The field applications of Artificial Intelligence have increased dramatically in the past few years. ANN is built from a large number of processing elements that individually deal with pieces of a big problem. If new data of the problem are presented to the system, the ANN can use the learned data to predict outcomes without any specific programming relating to the category of events involved. A large variety of possible ANN applications now exist for non ¬computer specialists. Therefore, with a very modest knowledge of the theory behind ANN, it is possible to tackle complicated problems in a researcher's own area of specialty with the ANN techniques. ANN learning occurs through training to a set of input and output data, where the

• Publication Date: 05-Jan-2014
• DOI: 10.15224/978-981-07-8859-9-07
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• Downloads: 0