The process of extracting valuable and novel insights from raw data involves
a series of complex steps. In the realm of Automated Machine Learning (AutoML),
a significant research focus is on automating aspects of this process,
specifically tasks like selecting algorithms and optimising their
hyper-parameters. A particularly challenging task in AutoML is automatic
workflow composition (AWC). AWC aims to identify the most effective sequence of
data preprocessing and ML algorithms, coupled with their best hyper-parameters,
for a specific dataset. However, existing AWC methods are limited in how many
and in what ways they can combine algorithms within a workflow.
Addressing this gap, this paper introduces EvoFlow, a grammar-based
evolutionary approach for AWC. EvoFlow enhances the flexibility in designing
workflow structures, empowering practitioners to select algorithms that best
fit their specific requirements. EvoFlow stands out by integrating two
innovative features. First, it employs a suite of genetic operators, designed
specifically for AWC, to optimise both the structure of workflows and their
hyper-parameters. Second, it implements a novel updating mechanism that
enriches the variety of predictions made by different workflows. Promoting this
diversity helps prevent the algorithm from overfitting. With this aim, EvoFlow
builds an ensemble whose workflows differ in their misclassified instances.
To evaluate EvoFlow's effectiveness, we carried out empirical validation
using a set of classification benchmarks. We begin with an ablation study to
demonstrate the enhanced performance attributable to EvoFlow's unique
components. Then, we compare EvoFlow with other AWC approaches, encompassing
both evolutionary and non-evolutionary techniques. Our findings show that
EvoFlow's specialised genetic operators and updating mechanism substantially
outperform current leading methods[..]

自动机器学习，自动工作流组合，EvoFlow，基因操作符，集成

基于语法的演化方法实现领域操作符和集成多样性的自动化工作流组合

Grammar-based evolutionary approach for automated workflow composition  with domain-specific operators and ensemble diversity

There are two distinct approaches to solving reinforcement learning problems,
namely, searching in value function space and searching in policy space.
Temporal difference methods and evolutionary algorithms are well-known examples
of these approaches. Kaelbling, Littman and Moore recently provided an
informative survey of temporal difference methods. This article focuses on the
application of evolutionary algorithms to the reinforcement learning problem,
emphasizing alternative policy representations, credit assignment methods, and
problem-specific genetic operators. Strengths and weaknesses of the
evolutionary approach to reinforcement learning are presented, along with a
survey of representative applications.

本文针对强化学习问题，重点介绍了进化算法在该问题上的应用，以及替代策略表述、信誉分配方法和问题特定遗传算子等方面的思考，在总结代表性应用的同时呈现了进化方法在强化学习中的优势和劣势。