Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic algorithms + data structures = evolution programs (3rd ed.)
Genetic Algorithms and Fuzzy Multiobjective Optimization
Genetic Algorithms and Fuzzy Multiobjective Optimization
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Computers and Industrial Engineering
AllelesLociand the Traveling Salesman Problem
Proceedings of the 1st International Conference on Genetic Algorithms
Systems and Computers in Japan
Design for Six Sigma through collaborative multiobjective optimization
Computers and Industrial Engineering
Reliability analysis of waste clean-up manipulator using genetic algorithms and fuzzy methodology
Computers and Operations Research
Efficient Optimization of Reliable Two-Node Connected Networks: A Biobjective Approach
INFORMS Journal on Computing
Reliability analysis of complex multi-robotic system using GA and fuzzy methodology
Applied Soft Computing
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A genetic algorithm approach is used to solve a multi-objective discrete reliability optimization problem in a k dissimilar-unit non-repairable cold-standby redundant system. Each unit is composed of a number of independent components with generalized Erlang distributions arranged in a series-parallel configuration. There are multiple component choices with different distribution parameters available for being replaced with each component of the system. The objective of the reliability optimization problem is to select the best components, from the set of available components, to be placed in the standby system in order to minimize the initial purchase cost of the system, maximize the system MTTF (mean time to failure), minimize the system VTTF (variance of time to failure) and also maximize the system reliability at the mission time. Finally, we apply a genetic algorithm with double strings using continuous relaxation based on reference solution updating (GADSCRRSU) to solve this multi-objective problem, using goal attainment formulation. The results are also compared against the results of a discrete-time approximation technique to show the efficiency of the proposed GA approach.