BECAS
ROJAS matias Gabriel
artículos
Título:
A Memetic Cellular Genetic Algorithm for Cancer Data Microarray Feature Selection
Autor/es:
MATIAS GABRIEL ROJAS; ANA CAROLINA OLIVERA; JESSICA ANDREA CARBALLIDO; PABLO JAVIER VIDAL
Revista:
IEEE LATIN AMERICA TRANSACTIONS
Editorial:
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Referencias:
Lugar: New York; Año: 2020 vol. 18 p. 1874 - 1883
ISSN:
1548-0992
Resumen:
Gene selection aims at identifying a -small- subset of informative genes from the initial data to obtain high predictive accuracy for classification in human cancers. Gene selection can be considered as a combinatorial search problem and thus can be conveniently handled with optimization methods. This paper proposes a Memetic Cellular Genetic Algorithm (MCGA) to solve the Feature Selection problem of cancer microarray datasets. Benchmark gene expression datasets, i.e., colon, lymphoma, and leukaemia available in the literature were used for experimentation. MCGA is compared with other well-known metaheuristic┬┤ strategies. The results demonstrate that our proposal can provide efficient solutions to find a minimal subset of the genes.