How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation.

Kostas Kouvaris,Jeff Clune,Markus Brede,Richard A Watson,Loizos Kounios,Andrey Rzhetsky

doi:10.1371/journal.pcbi.1005358

Kostas Kouvaris, Jeff Clune + Show 4 more

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https://doi.org/10.1371/journal.pcbi.1005358

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Abstract

One of the most intriguing questions in evolution is how organisms exhibit suitable phenotypic variation to rapidly adapt in novel selective environments. Such variability is crucial for evolvability, but poorly understood. In particular, how can natural selection favour developmental organisations that facilitate adaptive evolution in previously unseen environments? Such a capacity suggests foresight that is incompatible with the short-sighted concept of natural selection. A potential resolution is provided by the idea that evolution may discover and exploit information not only about the particular phenotypes selected in the past, but their underlying structural regularities: new phenotypes, with the same underlying regularities, but novel particulars, may then be useful in new environments. If true, we still need to understand the conditions in which natural selection will discover such deep regularities rather than exploiting ‘quick fixes’ (i.e., fixes that provide adaptive phenotypes in the short term, but limit future evolvability). Here we argue that the ability of evolution to discover such regularities is formally analogous to learning principles, familiar in humans and machines, that enable generalisation from past experience. Conversely, natural selection that fails to enhance evolvability is directly analogous to the learning problem of over-fitting and the subsequent failure to generalise. We support the conclusion that evolving systems and learning systems are different instantiations of the same algorithmic principles by showing that existing results from the learning domain can be transferred to the evolution domain. Specifically, we show that conditions that alleviate over-fitting in learning systems successfully predict which biological conditions (e.g., environmental variation, regularity, noise or a pressure for developmental simplicity) enhance evolvability. This equivalence provides access to a well-developed theoretical framework from learning theory that enables a characterisation of the general conditions for the evolution of evolvability.

Highlights

Linking the evolution of evolvability with generalisation in learning systems Explaining how organisms adapt in novel selective environments is central to evolutionary biology [1,2,3,4,5]
Living organisms are both robust and capable of change. The former property allows for stability and reliable functionality against genetic and environmental perturbations, while the latter provides flexibility allowing for the evolutionary acquisition of new potentially adaptive traits [5,6,7,8,9]. This capacity of an organism to produce suitable phenotypic variation to adapt to new environments is often identified as a prerequisite for evolvability, i.e., the capacity for adaptive evolution [7, 10, 11]
The general conditions which favour the emergence of adaptive developmental constraints that enhance evolvability are not wellunderstood. To address this we study the conditions where evolution by natural selection can find developmental organisations that produce what we refer to here as generalised phenotypic distributions—i.e., are these distributions capable of producing multiple distinct phenotypes that have been selected in the past, but they can produce novel phenotypes from the same family

Summary

Introduction

Linking the evolution of evolvability with generalisation in learning systems Explaining how organisms adapt in novel selective environments is central to evolutionary biology [1,2,3,4,5]. Living organisms are both robust and capable of change The former property allows for stability and reliable functionality against genetic and environmental perturbations, while the latter provides flexibility allowing for the evolutionary acquisition of new potentially adaptive traits [5,6,7,8,9]. This capacity of an organism to produce suitable phenotypic variation to adapt to new environments is often identified as a prerequisite for evolvability, i.e., the capacity for adaptive evolution [7, 10, 11]. In situations when selection can control phenotypic variation, it nearly always reduces such variation because it favours canalisation over flexibility [23, 27,28,29]

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