Sammuel Ramos da Silva
ABSTRACT
Evolutionary and Swarm algorithms show great effectiveness when performing feature selection, classification problems, and other optimization tasks. These scenarios highlight several algorithms such as Genetic Algorithm, Particle Swarm Optimization, Artificial Bee Colony, and Genetic Bee Colony (GBC). The last one is a combination of Genetic Algorithm and Artificial Bee Colony. Our study proposes an improvement over the GBC algorithm, including the Chaos Theory behavior in its foundation. In addition, we modified the Onlooker Bee and Scout Bee phase to improve the proposed model exploration and exploitation capabilities. Our novel proposal, Chaos-GBC, showed a very competitive performance compared to GBC and other metaheuristics. CGBC achieved the highest classification accuracy and the lowest average number of selected genes, showing the importance of our new proposal.
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Index Terms
- Chaotic genetic bee colony: combining chaos theory and genetic bee algorithm for feature selection in microarray cancer classification
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