preprints.org > biology and life sciences > other > doi: 10.20944/preprints202208.0402.v1

Preprint Short Note Version 1 Preserved in Portico This version is not peer-reviewed

Version 1 : Received: 22 August 2022 / Approved: 24 August 2022 / Online: 24 August 2022 (02:28:26 CEST)

Evolution by natural selection is often viewed as an optimisation process where an organism's phenotypic traits are adapted gradually to improve its fitness. Because of the many different traits with potentially conflicting requirements, among other factors, this optimisation process may appear onerous. Building on recent mathematical work connecting optima and simplicity, we here show that for certain generic phenotype fitness requirements --- those based on physics and engineering principles --- optimal phenotypic shapes will tend to `simple', in the sense of low algorithmic or descriptional complexity. As a result, we argue that adapting to these types of generic fitness requirements will be a much `easier' task for natural selection, compared to a null expectation based on arbitrary optimisation requirements. Further, selection's task may be easier still due to the fact that optimal phenotypes for one set of generic fitness constraints may also be close to optimal for other generic constraints, such that adapting to one constraint yields the other `for free'.

evolution; fitness; optimisation; algorithmic information theory

Biology and Life Sciences, Other

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