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@MASTERSTHESIS{Yu:905621,
      author       = {Yu, Jessica},
      title        = {{E}volving autonomous agents with simulated brains using
                      {L}2{L} and {N}etlogo},
      school       = {RWTH Aachen},
      type         = {Bachelorarbeit},
      address      = {Jülich},
      reportid     = {FZJ-2022-00853},
      pages        = {76 p},
      year         = {2021},
      note         = {Bachelorarbeit, RWTH Aachen, 2021},
      abstract     = {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.},
      cin          = {JSC / INM-6},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)INM-6-20090406},
      pnm          = {899 - ohne Topic (POF4-899) / HBP SGA3 - Human Brain
                      Project Specific Grant Agreement 3 (945539) / ICEI -
                      Interactive Computing E-Infrastructure for the Human Brain
                      Project (800858) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-899 / G:(EU-Grant)945539 /
                      G:(EU-Grant)800858 / G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)2},
      url          = {https://juser.fz-juelich.de/record/905621},
}