Reconstructability Analysis Detection of Optimal Gene Order in Genetic Algorithms

The building block hypothesis implies that genetic algorithm efficiency will be improved if sets of genes that improve fitness through epistatic interaction are near to one another on the chromosome. We demonstrate this effect with a simple problem, and show that information-theoretic reconstructability analysis can be used to decide on optimal gene ordering

Using Reconstructability Analysis for Input Variable Reduction: A Business Example

We demonstrate the use of reconstructability analysis (RA) on the UCI Australian Credit dataset to reduce the number of input variables for two different analysis tools. Using 14 variables, an artificial neural net (NN) is able to predict whether or not credit was granted, with a 79.1% success rate. RA preprocessing allows us to reduce the number of independent variables from 14 to two different sets of three, which have success rates of 77.2% and 76.9% respectively. The difference between these rates and that of the 14-variable NN is not statistically significant. The three-variable rulesets given by RA achieve success rates of 77.8% and 79.7%. Again, the difference between those values and the 14-variable NN is not statistically significant, that is, our approach provides a three-variable model that is competitive with the 14-variable equivalent

Using Reconstructability Analysis to Select Input Variables for Artificial Neural Networks

We demonstrate the use of Reconstructability Analysis to reduce the number of input variables for a neural network. Using the heart disease dataset we reduce the number of independent variables from 13 to two, while providing results that are statistically indistinguishable from those of NNs using the full variable set. We also demonstrate that rule lookup tables obtained directly from the data for the RA models are almost as effective as NNs trained on model variables

Ordering Genetic Algorithm Genomes With Reconstructability Analysis: Discrete Models

The building block hypothesis implies that genetic algorithm effectiveness is influenced by the relative location of epistatic genes on the chromosome. We demonstrate this with a discrete-valued problem, based on Kauffman’s NK model, and show that information-theoretic reconstructability analysis can be used to decide on optimal gene ordering