Abstract
In this paper, we investigate the use of artificial neural networks in order to solve the Shortest Common Superstring Problem. Concretely, the neural network used in this work is based on a multivalued model, MREM, very suitable for solving combinatorial optimization problems. We describe the foundations of this neural model, and how it can be implemented in the context of this problem, by taking advantage of a better representation than in other models, which, in turn, contributes to ease the computational dynamics of the model. Experimental results prove that our model outperforms other heuristic approaches known from the specialized literature.
Citation
J. M. Ortiz-de-Lazcano-Lobato, R. M. Luque, D. López-Rodríguez, et al. “Growing Competitive Network for Tracking Objects in Video Sequences”. In: Adaptive and Natural Computing Algorithms, 9th International Conference, ICANNGA 2009, Kuopio, Finland, April 23-25, 2009, Revised Selected Papers. Ed. by M. Kolehmainen, P. J. Toivanen and B. Beliczynski. Vol. 5495. Lecture Notes in Computer Science. Springer, 2009, pp. 109-118. DOI: 10.1007/978-3-642-04921-7_12. URL: https://doi.org/10.1007/978-3-642-04921-7_12.
BibTeX
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Papers citing this work
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- Manisha Kaushal, Baljit Singh Khehra, Akashdeep Sharma (2018). Soft Computing based object detection and tracking approaches: State-of-the-Art survey. Applied Soft Computing DOI