Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction

Stephen Friess1; Peter Tiňo1; Stefan Menzel2; Zhao Xu3; Bernhard Sendhoff2; Xin Yao4

10.1109/IJCNN55064.2022.9892483

2022

IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) / IEEE World Congress on Computational Intelligence (IEEE WCCI) / International Joint Conference on Neural Networks (IJCNN) / IEEE Congress on Evolutionary Computation (IEEE CEC)

2161-4393

978-1-6654-9526-4

2022 International Joint Conference on Neural Networks (IJCNN)

1-8

18-23 July 2022

Padua, Italy

345 E 47TH ST, NEW YORK, NY 10017 USA

IEEE

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 derandomized 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.

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

其他

英语

CPCI-S

European Union[766186]

Computer Science ; Engineering ; Neurosciences & Neurology

Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Engineering, Electrical & Electronic ; Neurosciences

WOS:000867070904117

IEEE

1.CERCIA, School of Computer Science, University of Birmingham, UK
2.Honda Research Institute Europe GmbH, Offenbach a.M., Germany
3.NEC Laboratories Europe GmbH, Heidelberg, Germany
4.Southern University of Science and Technology, Shenzhen, China

Stephen Friess,Peter Tiňo,Stefan Menzel,et al. Spatio-Temporal Activity Recognition for Evolutionary Search Behavior Prediction[C]. 345 E 47TH ST, NEW YORK, NY 10017 USA:IEEE,2022:1-8.