Overcoming Problems in the Measurement of Biological Complexity

TL;DR

This paper proposes a new method based on algorithmic information theory to measure biological complexity in genetic algorithms, addressing technical issues with traditional entropy computation, and compares entropy evolution in sexual versus asexual systems.

Contribution

It introduces a novel entropy measurement technique using Kolmogorov complexity for genetic algorithms, overcoming small population size limitations.

Findings

Differences in entropy evolution between sexual and asexual systems.

Validation of the Kolmogorov complexity approach for biological complexity.

Addressed technical problems in entropy measurement.

Abstract

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.