Investigating benchmark correlations when comparing algorithms with parameter tuning

Investigating benchmark correlations when comparing algorithms with parameter tuning
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在比较算法与参数调整时研究基准相关性

DOI:
10.1145/3205651.3205747
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发表时间:
2018
期刊:
--
影响因子:
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通讯作者:
Christie L
Christie L
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--
文献类型:
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作者:
Christie L

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基准对于比较优化算法的性能很重要,但是我们可以选择对我们的算法有利的实例,并忽略我们的算法表现不佳的实例。还涉及到算法的自动设计,它使用问题实例(基准)来训练算法:仔细选择的实例是需要的算法generalize.We扫描参数设置的差分进化适用于BBOB基准。几个基准函数高度相关。这可能会导致错误的结论,即算法在一般情况下表现良好,而在少数关键实例上表现不佳。这些相关性随评价次数而变化。
Benchmarks are important for comparing performance of optimisation algorithms, but we can select instances that present our algorithm favourably, and dismiss those on which our algorithm under-performs. Also related are automated design of algorithms, which use problem instances (benchmarks) to train an algorithm: careful choice of instances is needed for the algorithm to generalise.We sweep parameter settings of differential evolution to applied to the BBOB benchmarks. Several benchmark functions are highly correlated. This may lead to the false conclusion that an algorithm performs well in general, when it performs poorly on a few key instances. These correlations vary with the number of evaluations.
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