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- W2967768853 abstract "There are many swarms of creatures in nature, which lead to a lot of highly ordered and beautiful emergence behaviors. For the modeling of self-organizing rules, most of the existing literature focuses on modeling according to special knowledge of physics or biology. Some self-organizing models are proposed in the literature which has been validated by the reproduction of certain emergence motion pattern, such as torus, or flocking. However, there are few studies about datadriven modeling of self-organizing rules of swarms. In this paper, we propose a prior knowledge free (i.e., data-driven) approach to learn the self-organizing rules of moving swarms. We use a Genetic Programming (GP) based two-layer framework to optimize the self-organizing model which is consist of neighbor selection rules and corresponding reaction rules. The proposed data-driven modeling method is validated by modeling of three typical collective behaviors (highly parallel group, dynamic parallel group and torus swarm behavior) only according to the simulation data generated from Vicsek and Couzin models. An analysis is conducted with expression tree simplification, swarm behavior reproduction and global metric evaluation. Results show that the proposed method can learn classic self-organizing rules effectively." @default.
- W2967768853 created "2019-08-22" @default.
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- W2967768853 date "2019-06-01" @default.
- W2967768853 modified "2023-09-23" @default.
- W2967768853 title "A GP Based Two-Layer Framework for Data-Driven Modeling of Swarm Self-Organizing Rules" @default.
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- W2967768853 doi "https://doi.org/10.1109/cec.2019.8790126" @default.
- W2967768853 hasPublicationYear "2019" @default.
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