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001     905621
005     20240313095018.0
024 7 _ |a 2128/30612
|2 Handle
037 _ _ |a FZJ-2022-00853
041 _ _ |a English
100 1 _ |a Yu, Jessica
|0 P:(DE-HGF)0
|b 0
|e Corresponding author
245 _ _ |a Evolving autonomous agents with simulated brains using L2L and Netlogo
|f 2021-03-01 - 2021-09-30
260 _ _ |a Jülich
|c 2021
300 _ _ |a 76 p
336 7 _ |a bachelorThesis
|2 DRIVER
336 7 _ |a Thesis
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|2 EndNote
336 7 _ |a Output Types/Supervised Student Publication
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336 7 _ |a Bachelor Thesis
|b bachelor
|m bachelor
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336 7 _ |a MASTERSTHESIS
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336 7 _ |a SUPERVISED_STUDENT_PUBLICATION
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502 _ _ |a Bachelorarbeit, RWTH Aachen, 2021
|c RWTH Aachen
|b Bachelorarbeit
|d 2021
|o 2021-10-14
520 _ _ |a Artificial neural networks (ANNs) are popular machine learning techniques used to model autonomous agents. Spiking neural networks (SNNs) provide the ability to reproduce spatio-temporal dynamics by transmitting information through action potentials or spikes. Given their more biologically realistic characteristic, they are particularly attractive for modelling biological systems, including the analysis and understanding of biological self organisation. As with many neural models, the difficulty in achieving the desired performance is finding the appropriate parameters settings. A commonly used autonomous approach is given by genetic algorithms (GAs), which provide an evolution-based search technique inspired by natural adaptation processes. The performance of these meta-heuristic search techniques depends on the settings of its hyperparameters, which present a challenging task on their own.In this work, a multi-agent simulation model embedded in NetLogo is investigated. It simulates an artificial ant navigating through a virtual maze with many obstacles in search of food. Through this process the ant is controlled by an SNN, whose parameter optimisation is examined and optimised in this thesis using GAs of two different tools (L2L and BehaviorSearch). Afterwards, a deeper investigation on the optimized SNNs is covered to understand and explain the observed behavior in the simulation.
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536 _ _ |a HBP SGA3 - Human Brain Project Specific Grant Agreement 3 (945539)
|0 G:(EU-Grant)945539
|c 945539
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536 _ _ |a ICEI - Interactive Computing E-Infrastructure for the Human Brain Project (800858)
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536 _ _ |a SLNS - SimLab Neuroscience (Helmholtz-SLNS)
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856 4 _ |u https://juser.fz-juelich.de/record/905621/files/Bachelor%20Thesis.pdf
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910 1 _ |a Forschungszentrum Jülich
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981 _ _ |a I:(DE-Juel1)IAS-6-20130828

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