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Over the last decade, there has been a rapid growth in the generation and analysis of the genomics data. Though the existing data analysis methods are capable of handling a particular problem, they cannot guarantee to solve all problems with different nature. Therefore, there always lie a scope of a new algorithm to solve a problem which cannot be efficiently solved by the existing algorithms. In the present work, a novel hybrid approach is proposed based on the improved version of a recently developed bio-inspired optimization technique, namely, salp swarm algorithm (SSA) for microarray classification. Initially, the Fisher score filter is employed to pre-select a subset of relevant genes from the original high-dimensional microarray dataset. Later, a weighted-chaotic SSA (WCSSA) is proposed for the simultaneous optimal gene selection and parameter optimization of the kernel extreme learning machine (KELM …