When Optimization Is Just an Illusion

Abstract

Bio-inspired optimization algorithms have been successfully applied to solve many problems in engineering, science, and economics. In computer science bio-inspired optimization has different applications in different domains such as software engineering, networks, data mining, and many others. However, some applications may not be appropriate or even correct. In this paper we study this phenomenon through a particular method which applies the genetic algorithms on a time series classification task to set the weights of the similarity measures used in a combination that is used to classify the time series. The weights are supposed to be obtained by applying an optimization process that gives optimal classification accuracy. We show in this work, through examples, discussions, remarks, explanations, and experiments, that the aforementioned method of optimization is not correct and that completely randomly-chosen weights for the similarity measures can give the same classification accuracy.

A Weighted Minimum Distance Using Hybridization of Particle Swarm Optimization and Bacterial Foraging

“…Our main motivation in using BSOA is that this hybrid optimization technique is particularly designed to handle a multi-modal optimization problem which is the case with the problem at hand as we showed in [14].…”