PhD Studentship Evolution Inspired Development of Multi-level Selection Genetic Algorithm for General Applications

A new PhD position in evolutionary computation is available at University of Southampton. Candidates are welcome before April 01, 2019.

This is a cross-disciplinary project betweenthe School of Engineering and the School of Ocean and Earth Science.

Genetic algorithms are used in a range of diverse applications including: spacecraft antenna design, AI in computer games, determining which genes cause illness and portfolio selection for financial investments. Originally proposed by Turing in 1950, the genetic algorithm was first used by Holland in the 1960's to investigate evolution but has since been successfully adapted to solve optimisation problems. There is a vibrant community of researcher and industrialists developing novel methods to improve the performance of these algorithms allowing more complex problems to solve and allowing their use in new applications.

Once such method was developed at the University of Southampton (10.1088/1748-3190/aad2e8 and 10.1016/j.swevo.2018.09.005) which shows leading performance as a general solver, meaning it performs well across a wide range of problems, and shows particularly good preservation of the diversity of the population. It implements the Multi-Level Selection Theory and co-evolutionary approaches to improve the performance of the algorithm and already shows leading performance on a number of benchmarking problems and real world applications. However, the method is still new and there are a number of improvements that can be made and so this project will focus on its continued development.

The inspiration for this algorithm is already heavily based in evolutionary theory based on the idea that mechanisms that accelerate the rate of evolution in the natural world, will similarly accelerate the speed of the genetic algorithm. In this project we will take inspiration from epigenetics, transgenerational plasticity, social evolution and multi-level selection theory to test if the performance of the genetic algorithm can be similarly enhanced. Fusing contemporary evolutionary theory with an already highly efficient genetic algorithm has the potential to revolutionise evolutionary computation.

Candidates must possess at least a 2:1 degree level (or equivalent) qualification and be willing to engage in cross-disciplinary research. The project is ideally suitable for a candidate with a computational science, biological science or mathematical background, though any applications that show a passion for computational algorithms and/or evolutionary theory will be received warmly. Relevant training will be supplied in any areas where the candidate feels weaker.

The project will be supervised by both Adam Sobey and Tom Ezard meaning that you will be a member of both the Fluid Structure Interactions group in the Faculty of Engineering and Physical Sciences and the Palaeoceanography and Palaeoclimate group in the Faculty of Environmental and Life Sciences. The work will also be linked to the evolution theme in the Institute for Life Sciences. You will be expected to produce at least two journal quality papers and will also be required to undertake the Faculty research student training programme.

If you wish to discuss any details of the project informally, please contact Dr. Adam Sobey, Fluid-Structure Interactions group, Email:, Tel: +44 (0) 2380 59 3769.

This 3 year studentship covers UK/EU level tuition fees and provides an annual tax-free stipend at the standard EPSRC rate, which is 14,777 for 2018/19.

The funding available is competitive and will only be awarded to an outstanding applicant. As part of the selection process, the strength of the whole application is taken into account, including academic qualifications, personal statement, CV and references.

For further guidance on funding, please contact

Click here to apply and select the programme – PhD in Engineering and the Environment. Please enter the title of the PhD Studentship in the application form.


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