Now showing items 1-2 of 2

  • Generating easy and hard problems using the Proximate Optimality Principle. 

    McCall, John; Christie, Lee A.; Brownlee, Alexander Edward Ian (ACM http://doi.acm.org/10.1145/2739482.2764890, 2015)
    MCCALL, J. A. W., CHRISTIE, L. A. and BROWNLEE, A. E. I, 2015. Generating easy and hard problems using the Proximate Optimality Principle. In: Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation (GECCO Companion '15). New York: ACM. pp. 767-768.
    We present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as "similar solutions have similar fitness". We explore definitions of ...
  • Generating easy and hard problems using the Proximate Optimality Principle. [Dataset] 

    McCall, John; Christie, Lee A.; Brownlee, Alexander Edward Ian (OpenAIR@RGU, 2015-07-11)
    MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the Proximate Optimality Principle. [Dataset]. Held on OpenAIR [online]. Available from: https://hdl.handle.net/10059/1407
    These data were gathered to investigate the hypothesis that coherent functions will be easy and anti-coherent functions will be hard for a hillclimber. We generated 10 coherent functions for each length on bit-strings of ...