Evolutionary algorithm parameter tuning with sensitivity analysis
SIIS'11 Proceedings of the 2011 international conference on Security and Intelligent Information Systems
Solving very large instances of the scheduling of independent tasks problem on the GPU
Journal of Parallel and Distributed Computing
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This work proposes a new parallel asynchronous cellular genetic algorithm model for multi-core processors. The algorithm has been used for solving the DNA fragment assembly problem with the aim of finding highly accurate solutions in short computation times. This NP-Complete problem lies in reconstructing a DNA chain from multiple fragments that have previously been sequenced in a laboratory. The considered problem is a critical step in any genomic project, since the resulting chains are the basis for the entire work. Therefore, the quality of these chains is a major aspect for the correct development of the project. The proposed algorithm is able to find highly accurate results much faster than the other algorithms in the literature. Additionally, since it is parallel, it could be scalable to much larger problem instances, for which the methods typically used usually encounter difficulties. Finally, several new local search methods have been designed, and their influence on the performance of the algorithm has been analyzed.