长期以来，由于缺乏有效的定量表征方法，我们一直不能对演化算法已有的丰富的研究成果进行系统科学地归纳与分类，因而无法建立起科学的演化算法研究体系，这成为目前制约演化计算发展的一个重要瓶颈。近年，机器学习领域、尤其是深度学习方向上的突破性研究进展，为我们研究建立演化算法动态行为分析与定量表征体系开启了一个新的研究思路。本项目提出基于深度表征学习的最新理论与方法，研究演化算法动态行为分析与定量表征方法，重点研究：演化算法群体空间分布定量表征方法，演化算法动态行为分析与定量表征方法, 面向算法研究的演化算法深度表征体系构建方法，上述分析与表征方法在大规模优化演化算法研究中的应用，以及建立开放共享的演化算法分析工具平台。系统全面的演化算法定量表征与分析体系是建立科学的演化算法研究体系的必要前提，本项目从一个新的角度开展研究，具有重要的学术意义和一定的原创性。

Due to the lack of effective quantitative representation and analysis methods, we cannot make systematic and scientific conclusion and classification on huge amount of research results on Evolutionary Algorithms (EAs), therefore can not construct a scientific knowledge framework for the research of EAs, this has been a bottleneck in the development of evolutionary computation. Recently, the breakthrough in the research of machine learning, especially in its new branch the deep learning, has shown us a new approach to quantitative analysis and representation of dynamic behaviors of EAs. In this project, we propose to study methods for quantitative analysis and representation of EAs' dynamic behavior based on the up-to-date theory and techniques of deep representation learning. The focuses will be put on the study of: the methods for quantitative representation of EA's population distribution in the search space; methods for quantitative analysis and representation of EA's dynamic behaviors; methods for constructing Algorithmic Research-Oriented deep representation framework of EAs; Demonstration of applying above methods to the research of EAs for Large Scale Numerical Optimization; and the establishment of an open platform of tools for EA analysis. A systematic and comprehensive framework of quantitative analysis and representation of EAs is a prerequisite for constructing the algorithmic research framework of EAs. This project proposes to study it via a new approach, which is of important academic value and originality.