A self-adaptive binary cat swarm optimization using new time-varying transfer function for gene selection in DNA microarray expression cancer data

Microarray technology is beneficial in terms of diagnosing various diseases, including cancer. Despite all DNA microarray benefits, the high number genes versus low samples has always been a crucial challenge for this technology. Accordingly, we need new optimization algorithms to select optimal faster disease diagnosis. In article, version binary cat algorithm, named SBCSO, gene selection expression cancer data presented. The main contributions paper are listed as follows: First, opposition-based learning (OBL) mechanism employed improve proposed algorithm's population members' diversity. Second, time-varying V-shaped transfer function balance two phases exploration and extraction algorithm. Third, MR λ parameters algorithm adapted over time, finally, single-objective multi-objective approaches solve problems. 15 datasets pertinent types compare method with other well-known algorithms. experiments' results indicate that better capability