A Preliminary Study on Adaptive Evolution Control Using Rank Correlation for Surrogate-Assisted Evolutionary Computation
A Preliminary Study on Adaptive Evolution Control Using Rank Correlation for Surrogate-Assisted Evolutionary Computation
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DOI：
10.4018/ijsi.2018100105
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发表时间：
2018-10
期刊：
影响因子：
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通讯作者：
Yudai Kuwahata;J. Kushida;S. Ono
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文献类型：
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作者：
Yudai Kuwahata;J. Kushida;S. Ono
本文描述了代理辅助进化计算（SAEC）如何广泛应用于近似昂贵的优化问题，这些问题需要大量的计算时间，例如一个解决方案评估需要几个小时。SAEC还可能减少在几秒钟或几分钟内评估解决方案的廉价优化问题的处理时间。为了实现这一点，在优化过程中，目标函数的近似模型构建应该尽可能少地迭代。因此，本文提出了一种利用目标函数实际评价值与近似评价值之间的秩相关关系的自适应进化控制机制。然后使用这些相关性自适应地切换近似和实际评估阶段，减少学习近似模型所需的运行次数。实验表明，该方法可以有效地减少一些基准函数的处理时间。
This article describes how surrogate-assisted evolutionary computation (SAEC) has widely applied to approximate expensive optimization problems, which require much computational time such as hours for one solution evaluation. SAEC may potentially also reduce the processing time of inexpensive optimization problems wherein solutions are evaluated within a few seconds or minutes. To achieve this, the approximation model construction for an objective function should be iterated as few times as possible during optimization. Therefore, this article proposes an adaptive evolution control mechanism for SAEC using rank correlations between actually evaluated and approximately evaluated values of the objective function. These correlations are then used to adaptively switch the approximation and actual evaluation phases, reducing the number of runs required to learn the approximation model. Experiments show that the proposed method could successfully reduce the processing time in some benchmark functions even under inexpensive scenario.