Genetic Algorithm-Based Redundancy Optimization Problems in Fuzzy Framework

DOI10.1080/03610920600728716zbMath1274.90521OpenAlexW2154541535MaRDI QIDQ3424216

Fujun Hou, Qizong Wu

Publication date: 15 February 2007

Published in: Communications in Statistics - Theory and Methods (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610920600728716

zbMATH Keywords

genetic algorithmcredibility measurefuzzy simulationpossibility measureredundancy optimization

Mathematics Subject Classification ID

Approximation methods and heuristics in mathematical programming (90C59) Fuzzy and other nonstochastic uncertainty mathematical programming (90C70) Problem solving in the context of artificial intelligence (heuristics, search strategies, etc.) (68T20)

Cites Work

• Redundancy optimization problems with uncertainty of combining randomness and fuzziness
• Fuzzy sets as a basis for a theory of possibility
• Chance constrained programming with fuzzy parameters
• Theory and practice of uncertain programming
• Uncertainty theory. An introduction to its axiomatic foundations.
• Solving the redundancy allocation problem using a combined neural network/genetic algorithm approach
• A note on chance constrained programming with fuzzy coefficients

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