Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction

Stephen FRIESS, Peter TINO, Stefan MENZEL, Zhao XU, Bernhard SENDHOFF, Xin YAO

Research output: Book Chapters | Papers in Conference ProceedingsConference paper (refereed)Researchpeer-review

Abstract

Traditional methods for solving problems within computer science rely mostly upon the application of handcrafted algorithms. As however manual engineering of them can be considered to be a tedious process, it is interesting to consider how far internal mechanisms can be directly learned in an end-to-end manner instead. This is especially tempting to consider for metaheuristic and evolutionary optimization routines which inherently rely upon creating abundant amounts of data during run-time. To implement such an approach for these types of algorithms, it effectively requires a pipeline to first acquire deran-domized algorithm components in a domain-dependent manner and secondly a mapping to select them based upon characteristic features which unveil the black box character of an optimization problem. While in principle, within our prior work we proposed methods for extracting spatial features from metadata, these unfortunately fail to acknowledge the time-dependent nature of it. Thus, fail in scenarios when the inputs generated from initial iterations are not expressive enough. For this reason we specifically develop within this work architectures for spatio-temporal data processing. Particularly, we find that our proposed GCN-GRU and LSTM architectures, which take inspiration from CNN-LSTMs originally proposed for activity recognition in multimedia data-streams, demonstrate high efficiency and most consistent performance on time series of variable length. Further, we can also demonstrate that the class activation map (CAM) for interpretable learning with time series data helps to understand and reflects problem-dependent properties of the search behavior of an optimization algorithm. © 2022 IEEE.
Original languageEnglish
Title of host publicationProceedings of the International Joint Conference on Neural Networks
PublisherInstitute of Electrical and Electronics Engineers Inc.
Volume2022-July
ISBN (Print)9781728186719
DOIs
Publication statusPublished - 18 Jul 2022
Externally publishedYes

Bibliographical note

This research has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement number 766186 (ECOLE). It was also supported by the Research Institute of Trustworthy Autonomous Systems (RITAS), the Guangdong Provincial Key Laboratory (Grant No. 2020B121201001), the Program for Guangdong Introducing Innovative and Enterpreneurial Teams (Grant No. 2017ZT07X386), the Shenzhen Science and Technology Program (Grant No. KQTD2016112514355531)

Keywords

  • Activity Recognition
  • Algorithm Selection
  • Graph Neural Networks
  • Representation Learning
  • Time Series Classification

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