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- W2334152375 abstract "The purpose of the research presented in this paper is to formulate and develop the Variable Population- Based Sampling (VPBS) method, which is a variation on the Population-Based Sampling (PBS) technique that was developed by the authors of this paper. PBS is a Genetic Algorithm (GA) based approach that allows for discrete design optimization under uncertainty and requires a computational cost that is two orders of magnitude less than the state-of-the-art method that couples the GA with Monte Carlo Sampling (MCS). The PBS approach uses the population-based search nature of the GA to provide samples to statistically evaluate aggregate, probabilistic fitness functions. VPBS combines the concepts of PBS and MCS and requires even lower computational cost than that of PBS. The motivation is to implement the probabilistic approaches for the reliability-based, conceptual design of spacecraft systems that involve uncertain component failure rates so as to avoid large design margins and unnecessary high levels of redundancies which result from Deterministic Margin-Based (DMB) approaches. The paper compares the PBS method to the DMB and the MCS approaches and then compares the suggested VPBS approach to PBS. NOMENCLATURE c penalty multiplier" @default.
- W2334152375 created "2016-06-24" @default.
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- W2334152375 date "2004-01-05" @default.
- W2334152375 modified "2023-10-16" @default.
- W2334152375 title "Variable Population-Based Sampling for Probabilistic Design Optimization with a Genetic Algorithm" @default.
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- W2334152375 doi "https://doi.org/10.2514/6.2004-452" @default.
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