  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.
