Journal ArticleDOI
Gene selection for cancer classification by combining minimum redundancy maximum relevancy and bat-inspired algorithm
TLDR
The bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets and achieves comparable results of some datasets and produced new results for one dataset.Abstract:
In this paper, the bat-inspired algorithm (BA) is tolerated to gene selection for cancer classification using microarray datasets. Microarray data consists of irrelevant, redundant, and noisy genes. Gene selection problem is tackled by determining the most informative genes taken from microarray data to accurately diagnose the cancer disease. Gene selection problem is widely solved by optimisation algorithms. BA is a recent swarm-based algorithm, which imitates the echolocation system of bat individuals. It has been successfully applied to several optimisation problems. Gene selection is tackled by combining two stages, namely, filter stage, which uses Minimum Redundancy Maximum Relevancy (MRMR) method; and wrapper stage, which uses BA and SVM. To test the accuracy performance of the proposed method, ten microarray datasets were used. For comparative evaluation, the proposed method was compared with popular gene selection methods. The proposed method achieves comparable results of some datasets and produced new results for one dataset.read more
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Hybrid clustering analysis using improved krill herd algorithm
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A combination of objective functions and hybrid Krill herd algorithm for text document clustering analysis
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Group search optimizer: a nature-inspired meta-heuristic optimization algorithm with its results, variants, and applications
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From ants to whales: metaheuristics for all tastes
TL;DR: Some of the most popular nature-inspired optimization methods currently reported on the literature are analyzed, while also discussing their applications for solving real-world problems and their impact on the current literature.
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3D mesh simplification with feature preservation based on Whale Optimization Algorithm and Differential Evolution
TL;DR: A feature-preservation edge collapse operation to maintain the feature edges and a novel optimization algorithm, WOA-DE, by replacing the exploration phase of the original Whale Optimization Algorithm with the mutate and crossover operations of Differential Evolution to compute the optimal simplified mesh model more efficiently.