Measuring Biological Complexity in Digital Organisms

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5 Author(s)

We define biological complexity as the genetic information that an organism has about its environment. We have significantly improved methods to measure complexity based on Sharmon Information Theory and the principle of mutation-selection balance from population genetics. The previous method of Adami et al. was a population-based measure; it examined the information content of all genomes corresponding to the same phenotype. This population-based method had inherent limitations to its ability to approximate complexity: it requires a full population that must be at equilibrium, genomes must be fixed-length, and the environment must have only a single niche. Our new method overcomes these difficulties because it is genome-based rather than population-based. We approximate the total information in a genome as the sum of the information at each locus. The information content of a position is calculated by testing all of the possible mutations at that position and measuring the expected frequencies of potential genes in the mutation-selection equilibrium state. We discuss how this method reveals the way information is embedded in the organism during the evolutionary process.