Differential Evolution with Generalized Mutation Operator for Parameters Optimization in Gene Selection for Cancer Classification

TL;DR

This paper introduces GMDE, a generalized mutation operator for Differential Evolution, enhancing gene selection for cancer classification by combining exploration and exploitation strategies, and demonstrating superior performance on benchmarks and real-world gene expression data.

Contribution

The paper proposes a novel generalized mutation operator for Differential Evolution, combining multiple mutation strategies to improve optimization in gene selection for cancer classification.

Findings

GMDE outperforms traditional DE on benchmark functions.

Enhanced gene selection accuracy in cancer classification.

Significant improvement in microarray gene expression analysis.

Abstract

Differential Evolution (DE) proved to be one of the most successful evolutionary algorithms for global optimization purposes in continuous problems. The core operator in DE is mutation which can provide the algorithm with both exploration and exploitation. In this article, a new notation for DE is proposed which has a formula that can be utilized for generating and extracting novel mutations and by applying this new notation, four novel mutations are proposed. More importantly, by combining these novel trial vector generation strategies and four other well-known ones, we proposed Generalized Mutation Differential Evolution (GMDE) that takes advantage of two mutation pools that have both explorative and exploitative strategies inside them. Results and experimental analysis are performed on CEC2005 benchmarks and the results stated that GMDE is surprisingly competitive and significantly improved the performance of this algorithm. Finally, GMDE is also applied to parameters optimization, modification and improvement of a feature selection method for cancer classification purposes over gene expression microarray profiles.