Multidimensional statistical analysis of the parameterization of a genetic algorithm for the optimal ordering of tables

The optimal table row and column ordering can reveal useful patterns to improve reading and interpretation. Recently, genetic algorithms using standard crossover and mutation operators have been proposed to tackle this problem. In this paper, we carry out an experimental study that adds to this genetic algorithm crossover and mutation operators specially designed to deal with permutations and includes other parameters (initialization, replacement policy, mutation and crossover rates and stopping criteria) not examined in previous works. A proper analysis of the results must take into account all the parameters simultaneously, since the wrong conclusions can be drawn by studying each separately from the others. This is why we propose a framework for a multidimensional analysis of the results. This includes multiple hypothesis testing and a regression tree that builds a parsimonious and predictive model of the suitable configurations of the parameters.