Ensemble mutation and multi-population-driven differential evolution for numerical optimization and feature selection of breast cancer

Abstract

Breast cancer stands as a critical health challenge for women worldwide, whose numbers continue to increase because of multiple complicating elements. The distribution of high-dimensional medical datasets creates an essential challenge for breast cancer diagnosis because it leads to reduced predictive model efficiency. This research develops an advanced version of the differential evolution (DE), named ensemble mutation and multi-population-driven differential evolution (EMMDE), for optimal feature selection. EMMDE unites multiple novel mechanisms that include population division and ensemble mutation rules, together with a time-varied geometrically diversified scheme to enhance the diversity in the population. The Gaussian binning approach creates a new guiding vector that enters the mutation rule to strike an equilibrium between exploration and exploitation, which promotes fast convergence and diverse population states. The first stage of the EMMDE testing employs standard and complex benchmark functions from the IEEE CEC2017 benchmark set, which demonstrates its outperform search ability. Later, the transfer function-based binary version is developed and validated over 10 benchmark datasets from the UCI repository and 4 datasets of breast cancer. Based on MCE and other performance metrics, it is experimentally verified that the proposed binary EMMDE algorithm achieves highly accurate and promising results for the feature selection problem. Comparison with 12 well-known and recent metaheuristics has demonstrated the impact of proposed strategies in dealing with diverse categories of numerical optimization and feature selection problems.