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Memetic micro-genetic algorithms for cancer data classification

2023; Elsevier BV; Volume: 17; Linguagem: Inglês

10.1016/j.iswa.2022.200173

2667-3053

Matías Gabriel Rojas, Ana Carolina Olivera, Jessica Andrea Carballido, Pablo Javier Vidal,

Metaheuristic Optimization Algorithms Research

Fast and precise medical diagnosis of human cancer is crucial for treatment decisions. Gene selection consists of identifying a set of informative genes from microarray data to allow high predictive accuracy in human cancer classification. This task is a combinatorial search problem, and optimisation methods can be applied for its resolution. In this paper, two memetic micro-genetic algorithms (MμV1 and MμV2) with different hybridisation approaches are proposed for feature selection of cancer microarray data. Seven gene expression datasets are used for experimentation. The comparison with stochastic state-of-the-art optimisation techniques concludes that problem-dependent local search methods combined with micro-genetic algorithms improve feature selection of cancer microarray data.