In this study we measure the compression of information in a simulated evolutionary system. We do the investigation taking introns in the genome into account. We mainly investigate evolution of linear computer code but also present results from evolution of tree structures as well as messy genetic algorithms. The size of solutions is an important property of any system trying to learn or adapt to its environment. The results show significant compression or constant size of exons during evolution--in contrast to the rapid growth of overall size. Our conclusion is that an built-in pressure towards low-complexity solutions is measurable in several simulated evolutionary systems which may account for the robust adaptation showed by these systems.
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