From MEGATON to RASCAL: Surfing the Parameter Space of Evolutionary Algorithms

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@Misc{Sipper:2017:arxiv,
  author =       "Moshe Sipper and Weixuan Fu and Karuna Ahuja and 
                 Jason H. Moore",
  title =        "From MEGATON to RASCAL: Surfing the Parameter Space of
                 Evolutionary Algorithms",
  year =         "2017",
  month =        "13 " # jun,
  keywords =     "genetic algorithms, genetic programming, Evolutionary
                 Algorithms, Meta-Genetic Algorithm, Parameter Tuning,
                 Hyper-Parameter",
  URL =          "https://arxiv.org/abs/1706.04119",
  size =         "17 pages",
  abstract =     "The practice of evolutionary algorithms involves a
                 mundane yet inescapable phase, namely, finding
                 parameters that work well. How big should the
                 population be? How many generations should the
                 algorithm run? What is the (tournament selection)
                 tournament size? What probabilities should one assign
                 to crossover and mutation? All these nagging questions
                 need good answers if one is to embrace success. Through
                 an extensive series of experiments over multiple
                 evolutionary algorithm implementations and problems we
                 show that parameter space tends to be rife with viable
                 parameters. We aver that this renders the life of the
                 practitioner that much easier, and cap off our study
                 with an advisory digest for the weary",
  notes =        "p14 ' -Don't spend too much time and resources on
                 tuning hyper-parameters.

                 – Random search is a good choice for said tuning.

                 – Robustness to hyper-parameter tuning is a desired
                 quality of an evolutionary algorithm ...

                 – ... and if your algorithm requires very specific
                 parameters, the chances of finding them are most likely
                 slim at best. You're essentially in a
                 needle-in-a-haystack situation in hyper-parameter
                 space. Rethink and regroup.'

                 See blog entry:
                 http://gpemjournal.blogspot.co.uk/2017/06/parameters-parameters-parameters_20.html",
}

Genetic Programming entries for Moshe Sipper Weixuan Fu Karuna Ahuja Jason H Moore

Citations