An alternative measurement of the entropy evolution of a genetic algorithm

Abstract

This is an electronic version of the paper presented at The European Simulation and Modelling Conference (ESM), held in Leicester (United Kingdom) on 2009In a genetic algorithm, fluctuations of the entropy of a genome over time are interpreted as fluctuations of the information that the genome’s organism is storing about its environment, being this reflected in more complex organisms. The computation of this entropy presents technical problems due to the small population sizes used in practice. In this work we propose and test an alternative way of measuring the entropy variation in a population by means of algorithmic information theory, where the entropy variation between two generational steps is the Kolmogorov complexity of the first step conditioned to the second one. We also report experimental differences in entropy evolution between systems in which sexual reproduction is present or absent.This work has been partially sponsored by MICINN, project TIN2008-02081/TIN and by DGUI CAM/UAM, project CCG08-UAM/TIC-4425