Project SAGE
Speed of Adaptation in Population Genetics and Evolutionary Computation
University of Nottingham
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Unified Theory


The tail of the male peacock is one example of the creative power of adaptation. Male peacocks evolved these features due to a form of sexual selection, in which females display a preference for exuberant patterns. How long does it take to evolve complex features such as this is one of the questions being tackled by the SAGE project.

Population Genetics

Living organisms show an extraordinary variety of complex adaptations, ranging from the basic molecular mechanisms that allow accurate replication and translation of DNA, through the regulatory programs that reliably develop multicellular organisms, to the cognitive abilities that allow us to begin to understand our own evolution.

Population genetics is the discipline that studies the adaptation of populations in biological evolution, and how it is shaped by basic forces like mutation, recombination, selection, and migration between sub-populations. Population genetics studies how evolution is shaped by basic forces such as mutation, selection, recombination, migration among sub-populations, and stochasticity; it forms the core of the modern understanding of evolutionary theory, the so called "modern synthesis".

Population genetics has a long tradition of mathematical modelling, starting in the 30s with the pioneering work of Fisher, Wright, Haldane and others, and is now a highly sophisticated field in which mathematical analysis plays a central role. Early work focused on simple deterministic models with small numbers of loci, aiming at understanding how the change in genotype frequencies in a population was affected by basic evolutionary forces. It has since branched out to investigate topics such as the evolution of sexual reproduction, the role of environmental fluctuations in driving genetic change, and how population evolve to become independent species.

Evolutionary Computation

Evolution can be seen as a computational process in which the state of a population is shaped and changed over time by the randomized forces of selection, mutation, and recombination. This perspective allows natural evolution to be characterized as an algorithmic process, but it also facilitates the design and implementation of algorithms that are inspired by natural evolution.

An evolutionary algorithm is a computer program that employs operators inspired by Darwinian principles to search a large state space. Evolutionary algorithms are a class of general-purpose algorithmic approaches that are suitable for situations where problem-specific knowledge is incomplete due to domain complexity or resource scarcity. In general, evolutionary algorithms operate by evolving a population of candidate solution structures, allowing them to be recombined and mutated in various ways, and guiding the search toward "fitter" solutions by the mechanism of selection. Evolutionary algorithms and processes can be rigorously studied as instances of randomized algorithms for which there already exist powerful analytical tools from the field of theoretical computer science.

Evolutionary Algorithm

Evolutionary algorithms apply variation and selection operators to "evolve" a population of candidate solutions. The time and computational resources needed until solutions of appropriately high quality or low cost is one of the central studies of the SAGE project.

Aims and Goals of SAGE

While population genetics and evolutionary computation are asking many similar questions, they have developed in almost complete isolation from each other, and have consequently led to separate approaches, each with their own strengths and limitations.

This project's ultimate goal is to give a unified, quantitative theory for the speed of adaptation that enables inter-disciplinary studies of artificial and biological evolutionby combining the complementary approaches from population genetics and evolutionary computation.
Synergising these two fields has potential for transformative impact in both fields, revolutionising their understanding of evolution and opening new possibilities for applications involving evolutionary processes. In particular, our quantitative theory will highlight how the speed of adaptation depends on parameters of evolution as well as parameters of the fitness landscape a population is adapting to. Thereby, it will reveal what variants and parameters of evolutionary processes are most effective for particular fitness landscapes, which is of high relevance for both fields.
By focusing on the speed of adaptation, SAGE will provide new tools for researchers trying to predict the future adaptation of natural populations.  This will have significant impact on a series of challenges PG is currently confronted with, as well as allowing it to address new problems:  
  • better methods for inferring evolutionary parameters from observed genetic changes, enabling better inferences about the evolutionary history of natural populations
  • designing better selection schemes, allowing for more effective strategies against the evolution of drug and pesticide resistance, improving the in vitro evolution of enzymes, quantifying the lifetime of engineered biological circuits, such as in synthetic biology, and increasing the efficiency of animal and crop breeding programs.
  • more efficient methods for the exploration and identification of features of fitness landscapes, allowing for the elucidation of many fundamental questions in PG.
  • a more general understanding of evolution, allowing PG to investigate non-genetic forms of evolution, such as cultural evolution, and the spread of technological innovations.

SAGE Project