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- W2920794693 abstract "Conventional algorithms such as gradient-based optimization methods usually struggle to deal with high-dimensional non-linear problems and often land up with local minima. Recently developed nature-inspired optimization algorithms are the best approaches for finding global solutions for combinatorial optimization problems like microarray datasets. In this article, a novel hybrid swarm intelligence-based meta-search algorithm is proposed by combining a heuristic method called conditional mutual information maximization with chaos-based firefly algorithm. The combined algorithm is computed in an iterative manner to boost the sharing of information between fireflies, enhancing the search efficiency of chaos-based firefly algorithm and reduces the computational complexities of feature selection. The meta-search model is implemented using a well-established classifier, such as support vector machine as the modeler in a wrapper approach. The chaos-based firefly algorithm increases the global search mobility of fireflies. The efficiency of the model is studied over high-dimensional disease datasets and compared with standard firefly algorithm, particle swarm optimization, and genetic algorithm in the same experimental environment to establish its superiority of feature selection over selected counterparts." @default.
- W2920794693 created "2019-03-22" @default.
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- W2920794693 date "2019-04-01" @default.
- W2920794693 modified "2023-10-03" @default.
- W2920794693 title "Modified Firefly Algorithm With Chaos Theory for Feature Selection" @default.
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- W2920794693 doi "https://doi.org/10.4018/ijsir.2019040101" @default.
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