G.R. Kramer

Wright State University, Dayton, OH, United States

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Publications (3)0 Total impact

  • G.R. Kramer, J.C. Gallagher
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    ABSTRACT: In this paper, the authors present a performance analysis of a mini-population evolutionary algorithm (EA) on a robot-locomotion control problem. The results indicate that the nature of the search space allows for the design of highly efficient search algorithms that could greatly outperform current hardware-amenable techniques. The authors additionally believe that these search characteristics may be inherent in many practical problems, making the results useful for the larger community.
    Evolutionary Computation, 2005. The 2005 IEEE Congress on; 10/2005
  • G.R. Kramer, J.C. Gallagher, M. Raymer
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    ABSTRACT: Previous work has demonstrated the efficacy and feasibility of the simulated population *cGA family for embedded EH applications. This paper introduces and discusses a new *cGA variant that increases search efficacy on a specific class of EH problems without increasing the amount of hardware required for implementation. We discuss the new EA variant and provide experimental verification of its superiority against both the Dejong benchmarks and a practical problem in the construction of an EH controller to suppress thermoacoustic instability. We also compare these results with similar population-based and a population-less EAs to help understand the effects of introducing a simulated population. We conclude with a brief discussion of possible implications for the EH community in general.
    Evolvable Hardware, 2004. Proceedings. 2004 NASA/DoD Conference on; 07/2004
  • G.R. Kramer, J.C. Gallagher
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    ABSTRACT: Recently, we proposed a neuromorphic intrinsic online evolvable hardware (EH) system designed to learn control laws of physical devices. Since we intend to eventually build this device using mixed signal VLSI techniques, and because we intend to address control applications in which small size and low power consumption are critical, we are extremely concerned with the design of physically compact devices. This paper focuses on the evolutionary algorithm (EA) portion of our proposed system. We discuss modifications to our previously reported *CGA that significantly increases its performance against dynamic optimization problems without significantly increasing the amount of hardware required for implementation. We demonstrate the efficacy of our improvement by testing against two series of moving peak benchmarks. We conclude with discussions of both the implications of our findings and our plans for future work.
    Evolvable Hardware, 2003. Proceedings. NASA/DoD Conference on; 08/2003

Publication Stats

16 Citations

Top Journals


  • 2003–2005
    • Wright State University
      • Department of Computer Science and Engineering
      Dayton, OH, United States