Women in Computational Intelligence - Key Advances and Perspectives on Emerging Topics
Women in Computational Intelligence - Key Advances and Perspectives on Emerging Topics
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计算智能领域的女性 - 新兴主题的主要进展和观点
DOI：
10.1007/978-3-030-79092-9_9
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
Hart E
中科院分区：
文献类型：
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作者：
Hart E
开发优化算法的标准方法往往涉及选择一种算法并对其进行调整，使其能够很好地处理感兴趣领域中的大量问题实例。一旦部署，该算法就保持静态，尽管暴露于大量进一步的示例实例，但仍无法改进。此外，如果正在解决的实例的特征随着时间的推移而发生变化，则调整后的算法可能会表现不佳。为了解决这个问题，我们提出了终身学习优化器，它（1）自主地、持续地改进其优化算法，以便它们随着经验而改进；（2）如果性能因不可预见的数据而下降，则生成新的算法。
Standard approaches to developing optimisation algorithms tend to involve selecting an algorithm and tuning it to work well on a large set of problem instances from the domain of interest. Once deployed, the algorithm remains static, failing to improve despite being exposed to a wealth of further example instances. Furthermore, if the characteristics of the instances being solved shift over time, the tuned algorithm is likely to perform poorly. To counter this, we propose the lifelong learning optimiser, which (1) autonomously and continually refines its optimisation algorithm(s) so that they improve with experience and (2) generates novel algorithms if performance drops in reaction to unforeseen data.