Furtwangen im Schwarzwald, Germany
Furtwangen im Schwarzwald, Germany

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Hindi M.M.,Computer Engineering and Computer Science | Yampolskiy R.V.,Computer Engineering and Computer Science
Proceedings of the 23rd Midwest Artificial Intelligence and Cognitive Science Conference, MAICS 2012 | Year: 2012

In this paper we present a hybrid technique that applies a genetic algorithm followed by wisdom of artificial crowds approach to solving the graph-coloring problem. The genetic algorithm described here utilizes more than one parent selection and mutation methods depending on the state of fitness of its best solution. This results in shifting the solution to the global optimum more quickly than using a single parent selection or mutation method. The algorithm is tested against the standard DIMACS benchmark tests while limiting the number of usable colors to the known chromatic numbers. The proposed algorithm succeeded at solving the sample data set and even outperformed a recent approach in terms of the minimum number of colors needed to color some of the graphs.


Mbock E.A.M.,Computer Engineering and Computer science
Proceedings - 2015 International Conference on Computational Science and Computational Intelligence, CSCI 2015 | Year: 2015

It becomes interesting to analyze and apply the features of reconfigurable computations because the concept allows algorithm creation. An important algorithm that resulted from this concept is the known matrix inverse computation. This algorithm is becoming common for matrix based computations because it is free from singularities in comparison with other "Gauβ" methods. However, this extreme ease computational requirement has a limitation. The generated matrix inverse are all upper triangular or lower triangular. Since there is a need to extend computations to any matrix, we then present some implementation aspects of the reconfigurable matrix inverse and an extension of the process that handles full matrix inverse. This research uses the results of the reconfigurable matrix inverse computations completes them and makes the process capable of generating full matrix inverse. © 2015 IEEE.

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