Physical Complexity of Variable Length Symbolic Sequences

TL;DR

This paper extends the concept of Physical Complexity to variable length sequences, providing a new measure for information storage efficiency and analyzing clustering in populations through simulations.

Contribution

It introduces an extension of Physical Complexity for variable length sequences and develops a measure for information storage efficiency.

Findings

Extended Physical Complexity is consistent with the original for fixed length sequences.

The new measure helps understand clustering within populations.

Simulations validate the extended measure's effectiveness.

Abstract

A measure called Physical Complexity is established and calculated for a population of sequences, based on statistical physics, automata theory, and information theory. It is a measure of the quantity of information in an organism's genome. It is based on Shannon's entropy, measuring the information in a population evolved in its environment, by using entropy to estimate the randomness in the genome. It is calculated from the difference between the maximal entropy of the population and the actual entropy of the population when in its environment, estimated by counting the number of fixed loci in the sequences of a population. Up to now, Physical Complexity has only been formulated for populations of sequences with the same length. Here, we investigate an extension to support variable length populations. We then build upon this to construct a measure for the efficiency of information storage, which we later use in understanding clustering within populations. Finally, we investigate our extended Physical Complexity through simulations, showing it to be consistent with the original.