Abstract
The analysis of big data, particularly from the biosciences, provides unique challenges to the methods used to analyse such data. Datasets such as those used in genome-wide association studies can have a very high number of variables/dimensions (e.g. 400,000+) and therefore modifications are required to standard methods to allow them to function correctly.
A variety of methods can be used for such problems, among them ant colony optimisation is a promising method, inspired by the way in which ants find the shortest path in nature. The selection of paths traditionally uses a roulette wheel which works well for problems of smaller dimensionality but breaks down when higher numbers of variables are considered. In this paper, a subset-based tournament selection ACO approach is proposed that is shown to outperform the roulette wheel-based approach for operations research problems of higher dimensionality in terms of the performance of the final solutions and execution time on problems taken from the literature.
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The work contained in this paper was supported by an EPSRC First Grant (EP/J007439/1).
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Sapin, E., Keedwell, E. (2014). A Subset-Based Ant Colony Optimisation with Tournament Path Selection for High-Dimensional Problems. In: Nguyen, N., Kowalczyk, R., Fred, A., Joaquim, F. (eds) Transactions on Computational Collective Intelligence XVII. Lecture Notes in Computer Science(), vol 8790. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-44994-3_12
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DOI: https://doi.org/10.1007/978-3-662-44994-3_12
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