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On the interaction of function, constraint and complexity in evolutionary systems

On the interaction of function, constraint and complexity in evolutionary systems
On the interaction of function, constraint and complexity in evolutionary systems
Biological evolution contains a general trend of increasing complexity of the most complex organisms. But artificial evolution experiments based on the mechanisms described in the current theory generally fail to reproduce this trend; instead, they commonly show systematic trends of complexity minimisation. In this dissertation we seek evolutionary mechanisms that can explain these apparently conflicting observations. To achieve this we use a reverse engineering approach by building computational simulations of evolution. One highlighted problem is that even if complexity is beneficial, evolutionary simulations struggle with apparent roadblocks that prevent them from scaling to complexity. Another is that even without roadblocks, it is not clear what drives evolution to become more complex at all. With respect to the former, a key roadblock is how to evolve ‘irreducibly complex’ or ‘nondecomposable’ functions. Evidence from biological evolution suggests a common way to achieve this is by combining existing functions – termed ‘tinkering’ or ‘building block evolution’. But in simulation this approach generally fails to scale across multiple levels of organisation in a recursive manner. We provide a model that identifies the problem hindering recursive evolution as increasing ‘burden’ in the form of ‘internal selection’ as joined functions become more complex. We show how having an ontological development process that occurs by local growth, as present in most complex biological organisms, resolves this problem, enabling evolution to occur recursively. Meanwhile, to understand what drives complexity in evolution we provide a model showing that under certain conditions a well-studied concept from the computational study of algorithms – complexity lower bounds – applies in evolution. The model shows how the ‘difference’ between the conditions required by an organism’s replicator and its external environment results in a minimum complexity floor that varies as the external environment changes. We find that selection in such a system produces a system-wide, overall trend of increasing complexity of the most complex organisms (as environments are colonised), coupled with local trends of complexity minimisation in individual environments (as evolution seeks to minimise its cost of resources) –thereby resolving the tension between biological observations and theoretical outcomes. Our simulations and analytic results demonstrate (a) how evolution can, when complexity is beneficial, scale to complexity over multiple organisational levels, and (b) the conditions in which complexity is beneficial in evolution. These models describe a set of phenotypic, ontogenetic and environmental conditions that are generally present in biological evolution, in which evolution consistently generates an overall trend of increasing complexity of the most complex organisms.
University of Southampton
Davies, Adam
5c959da0-2515-4b94-a7a0-274bbe6d850b
Davies, Adam
5c959da0-2515-4b94-a7a0-274bbe6d850b
Watson, Richard
ce199dfc-d5d4-4edf-bd7b-f9e224c96c75

Davies, Adam (2014) On the interaction of function, constraint and complexity in evolutionary systems. University of Southampton, Physical Sciences and Engineering, Doctoral Thesis, 204pp.

Record type: Thesis (Doctoral)

Abstract

Biological evolution contains a general trend of increasing complexity of the most complex organisms. But artificial evolution experiments based on the mechanisms described in the current theory generally fail to reproduce this trend; instead, they commonly show systematic trends of complexity minimisation. In this dissertation we seek evolutionary mechanisms that can explain these apparently conflicting observations. To achieve this we use a reverse engineering approach by building computational simulations of evolution. One highlighted problem is that even if complexity is beneficial, evolutionary simulations struggle with apparent roadblocks that prevent them from scaling to complexity. Another is that even without roadblocks, it is not clear what drives evolution to become more complex at all. With respect to the former, a key roadblock is how to evolve ‘irreducibly complex’ or ‘nondecomposable’ functions. Evidence from biological evolution suggests a common way to achieve this is by combining existing functions – termed ‘tinkering’ or ‘building block evolution’. But in simulation this approach generally fails to scale across multiple levels of organisation in a recursive manner. We provide a model that identifies the problem hindering recursive evolution as increasing ‘burden’ in the form of ‘internal selection’ as joined functions become more complex. We show how having an ontological development process that occurs by local growth, as present in most complex biological organisms, resolves this problem, enabling evolution to occur recursively. Meanwhile, to understand what drives complexity in evolution we provide a model showing that under certain conditions a well-studied concept from the computational study of algorithms – complexity lower bounds – applies in evolution. The model shows how the ‘difference’ between the conditions required by an organism’s replicator and its external environment results in a minimum complexity floor that varies as the external environment changes. We find that selection in such a system produces a system-wide, overall trend of increasing complexity of the most complex organisms (as environments are colonised), coupled with local trends of complexity minimisation in individual environments (as evolution seeks to minimise its cost of resources) –thereby resolving the tension between biological observations and theoretical outcomes. Our simulations and analytic results demonstrate (a) how evolution can, when complexity is beneficial, scale to complexity over multiple organisational levels, and (b) the conditions in which complexity is beneficial in evolution. These models describe a set of phenotypic, ontogenetic and environmental conditions that are generally present in biological evolution, in which evolution consistently generates an overall trend of increasing complexity of the most complex organisms.

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Published date: April 2014
Organisations: University of Southampton, Agents, Interactions & Complexity

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Local EPrints ID: 374145
URI: http://eprints.soton.ac.uk/id/eprint/374145
PURE UUID: 09b22cf1-0e60-4b67-96f8-3d670df877bb

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Date deposited: 16 Feb 2015 13:14
Last modified: 21 Mar 2019 17:31

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