  Accurate screening of cancer types is crucial for effective cancer detection
and precise treatment selection. However, the association between gene
expression profiles and tumors is often limited to a small number of biomarker
genes. While computational methods using nature-inspired algorithms have shown
promise in selecting predictive genes, existing techniques are limited by
inefficient search and poor generalization across diverse datasets. This study
presents a framework termed Evolutionary Optimized Diverse Ensemble Learning
(EODE) to improve ensemble learning for cancer classification from gene
expression data. The EODE methodology combines an intelligent grey wolf
optimization algorithm for selective feature space reduction, guided random
injection modeling for ensemble diversity enhancement, and subset model
optimization for synergistic classifier combinations. Extensive experiments
were conducted across 35 gene expression benchmark datasets encompassing varied
cancer types. Results demonstrated that EODE obtained significantly improved
screening accuracy over individual and conventionally aggregated models. The
integrated optimization of advanced feature selection, directed specialized
modeling, and cooperative classifier ensembles helps address key challenges in
current nature-inspired approaches. This provides an effective framework for
robust and generalized ensemble learning with gene expression biomarkers.
Specifically, we have opened EODE source code on Github at
https://github.com/wangxb96/EODE.
