Abstract
Fish School Search (FSS) is a recently-proposed metaheuristic inspired by the collective behavior of fish schools. In this paper, we provide a preliminary assessment of FSS while coping with the task of mining coherent and sizeable biclusters from gene expression and collaborative filtering data. For this purpose, experiments were conducted on two real-world datasets whereby the performance of FSS was compared with that exhibited by two other population-based metaheuristics, namely, Genetic Algorithm (GA) and Particle Swarm Optimization (PSO). The results achieved demonstrate the usefulness of FSS while tackling the biclustering problem.
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Menezes, L., Coelho, A.L.V. (2011). Mining Coherent Biclusters with Fish School Search. In: Tan, Y., Shi, Y., Chai, Y., Wang, G. (eds) Advances in Swarm Intelligence. ICSI 2011. Lecture Notes in Computer Science, vol 6729. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-21524-7_70
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DOI: https://doi.org/10.1007/978-3-642-21524-7_70
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