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- W62395306 abstract "Cat swarm optimization (CSO) is a novel meta-heuristic for evolutionary optimization algorithms based on swarm intelligence. CSO imitates the behavior of cats through two sub-modes: seeking and tracing. Previous studies have indicated that CSO algorithms outperform other well-known meta-heuristics, such as genetic algorithms and particle swarm optimization. This study presents a modified version of cat swarm optimization (MCSO), capable of improving search efficiency within the problem space. The basic CSO algorithm was integrated with a local search procedure as well as the feature selection of support vector machines (SVMs). Experimental results demonstrate that the proposed MCSO algorithm provides better results in less time than basic CSO algorithms." @default.
- W62395306 created "2016-06-24" @default.
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- W62395306 date "2014-01-01" @default.
- W62395306 modified "2023-10-02" @default.
- W62395306 title "Modified Cat Swarm Optimization Algorithm for Feature Selection of Support Vector Machines" @default.
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- W62395306 doi "https://doi.org/10.1007/978-94-017-8798-7_40" @default.
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