论文标题
用于开发癌症治疗模拟的新颖搜索
Novelty search employed into the development of cancer treatment simulations
论文作者
论文摘要
当自动化搜索被粘在本地Optima中时，传统的优化方法可能会受到阻碍。因此，已经提出了开放式搜索方法，例如新颖的搜索方法来解决这个问题。忽略目标，同时施加压力来发现新颖的解决方案可能会导致在实际问题中提供更好的解决方案。这里采用了新颖的搜索来优化Physicell模拟器下的靶向药物输送系统的模拟设计。使用了混合物镜方程，既包含有效的肿瘤治疗的实际目标，又包含可能解决方案的新颖性度量。研究了混合方程的两个组成部分的不同权重，以揭示每个组件的重要性。
Conventional optimization methodologies may be hindered when the automated search is stuck into local optima because of a deceptive objective function landscape. Consequently, open ended search methodologies, such as novelty search, have been proposed to tackle this issue. Overlooking the objective, while putting pressure into discovering novel solutions may lead to better solutions in practical problems. Novelty search was employed here to optimize the simulated design of a targeted drug delivery system for tumor treatment under the PhysiCell simulator. A hybrid objective equation was used containing both the actual objective of an effective tumour treatment and the novelty measure of the possible solutions. Different weights of the two components of the hybrid equation were investigated to unveil the significance of each one.
