The multi-dimensional archive of phenotypic elites (MAP-Elites) algorithm is a popular evolutionary algorithm which returns a highly diverse set of elite solutions. The population is separated into a grid-like feature space defined by user-specified feature dimensions where each cell of the grid corresponds to a unique behaviour combination. The algorithm is conceptually simple and effective at producing high-quality, diverse solutions, but it comes with a major limitation on its exploratory capabilities. With every added feature, the set of solutions grows exponentially, making high-dimensional feature spaces infeasible. This work proposes a way of increasing features with the novel Age-Layered MAP-Elites (ALME) algorithm where the population is separated into age-layers and each layer has its own feature space. By using different features in the layers, the population migrates up through the layers experiencing selective pressure towards different features. This algorithm is applied to a simulated intelligent agent environment where agents are controlled by genetic programming (GP) trees to observe interesting emergent behaviours in underexplored regions of the feature space. It is observed that ALME is capable of producing a high-quality and diverse set of solutions that is competitive with traditional MAP-Elites without the combinatorial explosion in the resulting number of solutions.

AWS Glue is Amazon's serverless data integration cloud service that makes it simple and cost effective to extract, clean, enrich, load, and organize data. Originally launched in August 2017, AWS Glue began as an extract-transform-load (ETL) service designed to relieve developers and data engineers of the undifferentiated heavy lifting needed to load databases, data warehouses, and build data lakes on Amazon S3. Since then, it has evolved to serve a larger audience including ETL specialists and data scientists, and includes a broader suite of data integration capabilities. Today, hundreds of thousands of customers use AWS Glue every month. In this paper, we describe the use cases and challenges cloud customers face in preparing data for analytics and the tenets we chose to drive Glue's design. We chose early on to focus on ease-of-use, scale, and extensibility. At its core, Glue offers serverless Apache Spark and Python engines backed by a purpose-built resource manager for fast startup and auto-scaling. In Spark, it offers a new data structure --- DynamicFrames --- for manipulating messy schema-free semi-structured data such as event logs, a variety of transformations and tooling to simplify data preparation, and a new shuffle plugin to offload to cloud storage. It also includes a Hivemetastore compatible Data Catalog with Glue crawlers to build and manage metadata, e.g. for data lakes on Amazon S3. Finally, Glue Studio is its visual interface for authoring Spark and Python-based ETL jobs. We describe the innovations that differentiate AWS Glue and drive its popularity and how it has evolved over the years.