Welcome to SU Scholar

SU Scholar is Southwestern University's institutional repository, preserving and providing access to content created by members of the University. It collects scholarly and creative works produced by faculty, students, and other members of the Southwestern University community. SU Scholar includes faculty working papers, journal articles, conference presentations, book chapters, and creative works. It also highlights the best in student work, including select capstone projects, Paideia Seminar work, and more.

SU Scholar is managed by the Information Services Division in support of scholarship at Southwestern University. It is open-access compliant, with content freely accessible and searchable via Google Scholar and other search engines. To learn more about publishing in SU Scholar, see this guide. To contact us, email suscholar@southwestern.edu.

Communities

Recently Added

  • Schrum, Jacob; Rollins, Alex C. (GECCO '17 Proceedings of the Genetic and Evolutionary Computation Conference Companion, 2017-07)
    Previous research using evolutionary computation in Multi-Agent Systems indicates that assigning fitness based on team vs. individual behavior has a strong impact on the ability of evolved teams of artificial agents to ...
  • Schrum, Jacob; Gillespie, Lauren E.; Gonzalez, Gabriela R. (Proceedings of the Genetic and Evolutionary Computation Conference, 2017-07)
    Intelligent agents have a wide range of applications in robotics, video games, and computer simulations. However, fully general agents should function with as little human guidance as possible. Specifically, agents should ...
  • Schrum, Jacob; McDonnell, Tyler; Andoni, Sari; Bonab, Elmira; Cheng, Sheila; Goode, Jimmie; Moore, Keith; Sellers, Gavin; Choi, Jun-Hwan (GECCO '18 Proceedings of the Genetic and Evolutionary Computation Conference, 2018-07)
    Neuroevolution is a powerful and general technique for evolving the structure and weights of artificial neural networks. Though neuroevolutionary approaches such as NeuroEvolution of Augmenting Topologies (NEAT) have been ...
  • Schrum, Jacob; Volz, Vanessa; Lucas, Simon M.; Smith, Adam; Liu, Jialin; Risi, Sebastian (Proceedings of the Genetic and Evolutionary Computation Conference, 2018-07)
    Generative Adversarial Networks (GANs) are a machine learning approach capable of generating novel example outputs across a space of provided training examples. Procedural Content Generation (PCG) of levels for video games ...
  • Schrum, Jacob; Tweraser, Isabel; Gillespie, Lauren E. (Proceedings of the Genetic and Evolutionary Computation Conference, 2018-07)
    Compositional Pattern Producing Networks (CPPNs) are a generative encoding that has been used to evolve a variety of novel artifacts, such as 2D images, 3D shapes, audio timbres, soft robots, and neural networks. This paper ...

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