Bi-objective Genetic Algorithm with Rough Set Theory for Important Gene Selection in Disease Diagnosis

DOI10.1007/978-981-13-1471-1_13zbMath1409.90236OpenAlexW2886148683MaRDI QIDQ3122308

Soumen Kumar Pati, Asit K. Das

Publication date: 20 March 2019

Published in: Multi-Objective Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-981-13-1471-1_13

zbMATH Keywords

cellular automataKullback-Leibler divergencerough set theorymulti-objective genetic algorithmmicroarray datagene selection

Mathematics Subject Classification ID

Multi-objective and goal programming (90C29) Approximation methods and heuristics in mathematical programming (90C59) Fuzzy and other nonstochastic uncertainty mathematical programming (90C70) Protein sequences, DNA sequences (92D20)

Uses Software

• MOEA/D

Cites Work

• Differential evolution. A practical approach to global optimization. With CD-ROM.
• Quasi-random initial population for genetic algorithms
• ROUGH SET THEORY AND ITS APPLICATIONS TO DATA ANALYSIS
• On Information and Sufficiency
• Unnamed Item

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