  In 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. As an example application of this technique, we report
experimental differences in entropy evolution between systems in which sexual
reproduction is present or absent.
