Reducing bias and inefficiency in the selection algorithm
Proceedings of the Second International Conference on Genetic Algorithms on Genetic algorithms and their application
Design of truss-structures for minimum weight using genetic algorithms
Finite Elements in Analysis and Design
An Introduction to Genetic Algorithms
An Introduction to Genetic Algorithms
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
First Course On Fuzzy Theory And Applications.
First Course On Fuzzy Theory And Applications.
Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing
Cost Optimization of Structures: Fuzzy Logic, Genetic Algorithms, and Parallel Computing
A neuro-fuzzy evaluation of steel beams patch load behaviour
Advances in Engineering Software
ICNC '09 Proceedings of the 2009 Fifth International Conference on Natural Computation - Volume 04
Quantum-inspired evolutionary algorithm for a class of combinatorial optimization
IEEE Transactions on Evolutionary Computation
Multiobjective topology optimization of truss structures with kinematic stability repair
Structural and Multidisciplinary Optimization
Hybrid fuzzy-genetic system for optimising cabled-truss structures
Advances in Engineering Software
On design-dependent constraints and singular topologies
Structural and Multidisciplinary Optimization
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This paper sheds light on a fuzzy-genetic system applied to optimize cabled-truss structures. The optimization procedure combines ground structure approach, nonlinear finite element analysis, genetic algorithm, and fuzzy logic. The latter is used to include expertise in the evolutionary search to classify and filter individuals with low survival possibility sp. The classification is based on a scale that varies from 0% to 100%, and the filtering depends on a threshold value Spt defined by the user. Particularly, the individuals with sp ≤ Spt are not evaluated, thereby decreasing the total number of evaluations. Although this approach proved suitable to reduce computational cost, the effect of different Spt values on the system's performance was not yet investigated. In that light, this work aims to present a sensitivity analysis of the fuzzy-genetic optimization system to variations of Spt. For that, the system was applied to ground structures with 10 elements and Spt values ranging from 0% to 100%. The results were compared by means of the analysis of variance test in order to investigate the effects of Spt on the system's performance and to identify the optimum Spt, which was found to be of 60% for the studied case.