"G-SELC: A New Global Optimization Technique Using Genetic Algorithms, Tabu Search and Gaussian Processes"

“G-SELC：一种使用遗传算法、禁忌搜索和高斯过程的新全局优化技术”

• 批准号: 0905731
• 负责人: Abhyuday Mandal
• 金额: $ 10万
• 依托单位: University of Georgia Research Foundation Inc
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2009
• 资助国家: 美国
• 起止时间: 2009-07-01 至 2013-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0905731&HistoricalAwards=false
• 关键词: SELC; New; Global; Optimization; Technique

This award is funded under the American Recovery and Reinvestment Act of 2009 (Public Law 111-5).In this work, the investigator develops a new global optimization technique, which is primarily motivated by applications in drug discovery. Although identification of useful compounds is critical to improving efficiency in drug discovery, pharmaceutical industries generally adopt ad hoc approaches to identify promising compounds. The proposed research aims to develop an efficient technique named G-SELC, which expedites this process. To this end, the investigator develops a global optimization procedure using local search techniques such as Genetic Algorithms and Tabu Search combined with statistical modeling involving Gaussian processes. This research is also extended to categorical variables in Gaussian process. In addition, the investigator develops efficient numerical techniques to reduce computational burden for this batch sequential optimization problem.Identifying promising compounds from a vast collection of feasible compounds is an important and yet challenging problem in pharmaceutical industry. The proposed research helps reduce the expenditure at the early stages of drug discovery, thus creating significant economic and social benefits. This optimization technique is also used to identify optimal solutions in many other scientific research problems such as computer experiments, functional magnetic resonance imaging and nanotechnology. The investigator shows that in some applications, categorical variables can be treated as continuous. This simplifies the computation in Gaussian process modeling significantly. It has far-reaching consequences not only in drug discovery, but also in complex computer modeling where Gaussian process modeling is used extensively which includes modeling air quality, calibration of computational models of cerebral blood flow, predicting climate and weather, statistical mechanics of granular flow, terrestrial models, dynamics of infectious diseases and so on.

该奖项由 2009 年美国复苏和再投资法案（公法 111-5）资助。在这项工作中，研究人员开发了一种新的全局优化技术，该技术主要受药物发现应用的推动。尽管鉴定有用的化合物对于提高药物发现的效率至关重要，但制药行业通常采用临时方法来鉴定有前途的化合物。拟议的研究旨在开发一种名为 G-SELC 的有效技术，以加快这一过程。为此，研究人员使用遗传算法和禁忌搜索等局部搜索技术，结合涉及高斯过程的统计建模，开发了全局优化程序。这项研究还扩展到高斯过程中的分类变量。此外，研究人员开发了有效的数值技术来减少该批量顺序优化问题的计算负担。从大量可行的化合物中识别有前途的化合物是制药行业中一个重要且具有挑战性的问题。拟议的研究有助于减少药物发现早期阶段的支出，从而创造显着的经济效益和社会效益。这种优化技术还用于确定许多其他科学研究问题的最佳解决方案，例如计算机实验、功能磁共振成像和纳米技术。研究人员表明，在某些应用中，分类变量可以被视为连续变量。这显着简化了高斯过程建模中的计算。它不仅在药物发现方面具有深远的影响，而且在广泛使用高斯过程模型的复杂计算机建模中，包括空气质量建模、脑血流计算模型校准、气候和天气预测、颗粒流统计力学、陆地模型、传染病动力学等。