Applying the triple parameter hypothesis to maintenance scheduling

  • Authors:

  • John Crofford;Brent E. Eskridge;Dean F. Hougen

  • Affiliations:

  • Southern Nazarene University, Bethany, OK, USA;Southern Nazarene University, Bethany, OK, USA;University of Oklahoma, Norman, OK, USA

  • Venue:
  • Proceedings of the 12th annual conference on Genetic and evolutionary computation
  • Year:
  • 2010

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Abstract

A method for identifying values for a genetic algorithm's probability of crossover, mutation rate, and selection pressure that promote the evolution of better results in fewer generations has recently been proposed. This approach, termed the Triple Parameter Hypothesis, uses schema theory to derive these values. However, in initial experimental tests of the hypothesis, the schema distances used were at the extreme ends of the spectrum. In the work presented here, we evaluate the parameters predicted by the hypothesis in a series of maintenance scheduling experiments which use a schema distance in between these extremes. Results show that evolutionary runs which use parameters that satisfy the hypothesis statistically significantly outperform runs that use parameters that do not satisfy the hypothesis, indicating that the hypothesis can be a general purpose process for identifying "good" parameter values