Incollection,
Robust Pareto Front Genetic Programming Parameter Selection Based on Design of Experiments and Industrial Data
Genetic Programming Theory and Practice IV, volume 5 of Genetic and Evolutionary Computation, chapter 2, Springer, Ann Arbor, (11-13 May 2006)
Abstract
Symbolic regression based on Pareto front GP is a very
effective approach for generating high-performance
parsimonious empirical models acceptable for industrial
applications. The chapter addresses the issue of
finding the optimal parameter settings of Pareto front
GP which direct the simulated evolution toward simple
models with acceptable prediction error. A generic
methodology based on statistical design of experiments
is proposed. It includes determination of the number of
replicates by half-width confidence intervals,
determination of the significant factors by fractional
factorial design of experiments, approaching the
optimum by steepest ascent/descent, and local
exploration around the optimum by Box Behnken design of
experiments. The results from implementing the proposed
methodology to different types of industrial data sets
show that the statistically significant factors are the
number of cascades, the number of generations, and the
population size. The optimal values for the three
parameters have been defined based on second order
regression models with R2 higher than 0.97 for small,
medium, and large-sized data sets. The robustness of
the optimal parameters toward the types of data sets
was explored and a robust setting for the three
significant parameters was obtained. It reduces the
calculation time by 30per cent to 50per cent without
statistically significant reduction in the mean
response.
Tags
- algorithms,
- applications,
- design
- experiments,
- genetic
- industrial
- of
- parameter
- programming,
- regression,
- selection
- symbolic
