Erkin Bahceci
Ph.D. Alumni
Erkin's work focused on understanding how multiple agents search for the same peaks on a common fitness landscape, or Competitive MultiAgent Search (CMAS). This problem is motivated by innovation search in the real world, i.e. how high-tech companies search for new products. Erkin simulated these processes in NK landscapes, used evolutionary computation to discover optimal strategies, and verified the discoveries using human subject data. After graduation (and even before it) he went to work at Google, Inc.
Evolving Strategies for Social Innovation Games Erkin Bahceci, Riitta Katila and Risto Miikkulainen To Appear In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2015),... 2015

Competitive Multi-Agent Search Erkin Bahceci PhD Thesis, Department of Computer Science, The University of Texas at Austin, Austin, TX 78712, Dec... 2014

Transfer of Evolved Pattern-Based Heuristics in Games Erkin Bahceci and Risto Miikkulainen In IEEE Symposium On Computational Intelligence and Games (CIG 2008), 220-227, Perth, Austral... 2008

Coevolving Strategies for General Game Playing Joseph Reisinger, Erkin Bahceci, Igor Karpov and Risto Miikkulainen In Proceedings of the {IEEE} Symposium on Computational Intelligence and Games, 320-327, Pis... 2007

NKVis This package contains a 3D visualization tool for NK fitness landscapes. Two types of visualizations are provided: a 2011

NEAT C++ The NEAT package contains source code implementing the NeuroEvolution of Augmenting Topologies method. The source code i... 2010

rtNEAT C++ The rtNEAT package contains source code implementing the real-time NeuroEvolution of Augmenting Topologies method. In ad... 2006