Modeling evolutionary algorithms for dynamic and stochastic optimization

动态和随机优化的进化算法建模

• 批准号: 238777-2010
• 负责人: Wineberg, Mark
• 金额: $ 1.09万
• 依托单位: University of Guelph
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2013
• 资助国家: 加拿大
• 起止时间: 2013-01-01 至 2014-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=524444
• 关键词: Modeling; evolutionary; algorithms; dynamic; stochastic

The aim of this research is to study and model the behavior of evolutionary algorithms (EA) in stochastic and dynamic environments, with an eye to better understand and so better control evolutionary behavior under these environments. The models used in this research are based on statistically significant observations of production-level EA systems as opposed to a bottom-up analysis of the mathematical (probabilistic) properties of a very simplified EA. Furthermore they are not just performance (output) models of the EA but also comprise the properties that make up the state and dynamics of the EA within a complex environment.

这项研究的目的是研究和模拟在随机和动态环境中进化算法（EA）的行为，以便更好地理解，从而更好地控制这些环境下的进化行为。这项研究中使用的模型基于对生产水平的EA系统的统计学意义观察，而不是对非常简化的EA的数学（概率）特性的自下而上分析。此外，它们不仅是EA的性能（输出）模型，还包括在复杂环境中构成EA的状态和动态的属性。