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- W2325831679 abstract "Stopping criterion for global optimization algorithms are generally based on closeness to global optimum. But when we have an expensive or time consuming objective function, we may not afford to proceed till convergence. This led us to look at worth-based stopping criterion that justifies continuing with the optimization only if the next cycle yields a specified improvement. Then the challenge was to devise effectiveness indicators that could measure the success of such a stopping criterion. In this work we propose three ways to measure stopping effectiveness: (i) number of wasted cycles, (ii) premature stopping of the algorithm, and (iii) net profit from executed cycles. These stopping effectiveness indicators are presented for worth-based stopping criterion for surrogate-based optimization algorithms, which are a popular choice for dealing with expensive objective functions. In this work we use two variants of the surrogate-based Efficient Global Optimization (EGO) algorithm: (i) using expected improvement (EGO-EI), (ii) using probability of improvement with adaptive target setting (EGO-AT)." @default.
- W2325831679 created "2016-06-24" @default.
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- W2325831679 date "2015-01-02" @default.
- W2325831679 modified "2023-09-25" @default.
- W2325831679 title "Effectiveness Indicators for Stopping Criteria based on Minimum Required Improvement" @default.
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- W2325831679 doi "https://doi.org/10.2514/6.2015-0901" @default.
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