Innovations in statistical analysis and genetic algorithms

Abstract

We proposed new techniques in statistics and in genetic algorithms. We used biological principles to innovate new approaches in genetic algorithms that yielded improved solutions to optimization problems, finding improved best known results for multiple instances. These mimicked patterns in the natural world, including female choice of mates, as well as alpha male social structures. We also highlight inconsistencies between biological processes and their genetic algorithm counter-parts. Two other innovations in methodology and statistics include our development of the sequential correlated Bonferroni test which controls for false positive results that occur from running multiple statistical tests. It incorporates the correlation between significant p-values, thereby resulting in a less conservative filter. We also developed the statistical underpinnings of a new approach for estimating transition points (in species or any other defined population) between stages. Transition from one stage to the next is a natural part of life, yet it can be difficult to estimate, particularly in cases where only a few transitions occur in every measurement period. We confirmed the validity and applicability of this new approach demonstrating low standard errors and robust output.