Design patterns (DPs) are recognised as a good practice in software
development. However, the lack of appropriate documentation often hampers
traceability, and their benefits are blurred among thousands of lines of code.
Automatic methods for DP detection have become relevant but are usually based
on the rigid analysis of either software metrics or specific properties of the
source code. We propose GEML, a novel detection approach based on evolutionary
machine learning using software properties of diverse nature. Firstly, GEML
makes use of an evolutionary algorithm to extract those characteristics that
better describe the DP, formulated in terms of human-readable rules, whose
syntax is conformant with a context-free grammar. Secondly, a rule-based
classifier is built to predict whether new code contains a hidden DP
implementation. GEML has been validated over five DPs taken from a public
repository recurrently adopted by machine learning studies. Then, we increase
this number up to 15 diverse DPs, showing its effectiveness and robustness in
terms of detection capability. An initial parameter study served to tune a
parameter setup whose performance guarantees the general applicability of this
approach without the need to adjust complex parameters to a specific pattern.
Finally, a demonstration tool is also provided.

设计模式是软件开发中被认可的良好实践，但是缺乏适当的文档常常使得追溯困难，其益处在庞杂的代码中变得模糊。我们提出了一种基于进化机器学习和多样性软件属性的新颖检测方法 GEML。通过进化算法提取出最能描述设计模式的特征，并建立基于规则的分类器来预测新代码中是否包含隐藏的设计模式实现。GEML 经过验证可以有效、稳健地检测多样性的设计模式。此外，还提供了演示工具。

GEML：基于语法的进化机器学习方法用于设计模式检测

GEML: A Grammar-based Evolutionary Machine Learning Approach for  Design-Pattern Detection

Evolutionary machine learning (EML) has been applied to games in multiple
ways, and for multiple different purposes. Importantly, AI research in games is
not only about playing games; it is also about generating game content,
modeling players, and many other applications. Many of these applications pose
interesting problems for EML. We will structure this chapter on EML for games
based on whether evolution is used to augment machine learning (ML) or ML is
used to augment evolution. For completeness, we also briefly discuss the usage
of ML and evolution separately in games.

进化机器学习已被应用于游戏的多个方面和不同目的。AI 在游戏中的研究不仅关乎玩游戏，还包括生成游戏内容、模拟玩家等多个应用，其中许多应用对于进化机器学习提出了有趣的问题。本章将根据进化是用于增强机器学习还是机器学习用于增强进化来结构化地讨论游戏中的进化机器学习。为了完整起见，我们还简要讨论了机器学习和进化在游戏中的分开使用。

进化机器学习与游戏

Evolutionary Machine Learning and Games

Evolutionary Computation (EC) has emerged as a powerful field of Artificial
Intelligence, inspired by nature's mechanisms of gradual development. However,
EC approaches often face challenges such as stagnation, diversity loss,
computational complexity, population initialization, and premature convergence.
To overcome these limitations, researchers have integrated learning algorithms
with evolutionary techniques. This integration harnesses the valuable data
generated by EC algorithms during iterative searches, providing insights into
the search space and population dynamics. Similarly, the relationship between
evolutionary algorithms and Machine Learning (ML) is reciprocal, as EC methods
offer exceptional opportunities for optimizing complex ML tasks characterized
by noisy, inaccurate, and dynamic objective functions. These hybrid techniques,
known as Evolutionary Machine Learning (EML), have been applied at various
stages of the ML process. EC techniques play a vital role in tasks such as data
balancing, feature selection, and model training optimization. Moreover, ML
tasks often require dynamic optimization, for which Evolutionary Dynamic
Optimization (EDO) is valuable. This paper presents the first comprehensive
exploration of reciprocal integration between EDO and ML. The study aims to
stimulate interest in the evolutionary learning community and inspire
innovative contributions in this domain.

这篇论文是关于进化计算、人工智能、进化机器学习、进化动态优化和机器学习之间互相结合的研究，旨在激发进化学习社区的兴趣并在该领域激发创新贡献。