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Anytime Analysis for Dynamic Optimisation Problems

   Department of Computer Science

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  Dr Thomas Jansen  No more applications being accepted  Funded PhD Project (Students Worldwide)

About the Project

The UKRI CDT in Artificial Intelligence, Machine Learning and Advanced Computing (AIMLAC) aims at forming the next generation of AI innovators across a broad range of STEMM disciplines. The CDT provides advanced multi-disciplinary training in an inclusive, caring and open environment that nurture each individual student to achieve their full potential. Applications are encouraged from candidates from a diverse background that can positively contribute to the future of our society. 

The UK Research and Innovation (UKRI) fully-funded scholarships cover the full cost of tuition fees, a UKRI standard stipend of £15,921 per annum and additional funding for training, research and conference expenses. The scholarships are open to UK and international candidates.

Closing date for applications is 12 February 2022. For further information on how to apply please click here and select the "UKRI CDT Scholarship in AIMLAC" tab.

Project Overview

Many optimisation problems are too difficult to be solved efficiently by standard algorithms. Heuristic optimisation methods like evolutionary algorithms or simulated annealing are frequently applied in these situations. The theory of these heuristics still puts an emphasis on runtime analysis and is at odds with the way these heuristics are actually applied. This project addresses this gap by concentrating on anytime analysis targeting dynamic problems that change over time.

 Building on existing anytime analysis results (sometimes also called fixed budget results [2]) as well as recent results from fixed target analysis [1], the project performs a systematic study of dynamic optimisation. The starting point are simple static unimodal and multimodal benchmarks [3] and simple different tools to construct dynamic optimisation problems from static ones. Starting from simple heuristics like random sampling and local search the tools and methods are developed to compare these baseline methods with more advanced methods, employing populations, crossover, and different approaches to deal with dynamic optimisation problems like hall of fame approaches or diploidy.


[1] M. Buzdalov, B. Doerr, C. Doerr, and D. Vinokurov (2020): Fixed-target runtime analysis. In Proceedings of the 2020 Genetic and Evolutionary Computation Conference (GECCO 2020), ACM Press, pages 1295-1303.
[2] T. Jansen (2018): Analysing stochastic search heuristics operating on a fixed budget. In B. Doerr, F. Neumann (Eds.): Theory of Evolutionary Computation—Recent Advances. Springer, pages 249-270.
[3] T. Jansen and C. Zarges (2016): Example landscapes to support analysis of multimodal optimisation. In Proceedings of the 14th International Conference on Parallel Problem Solving From Nature (PPSN XIV). Springer, LNCS 9921, pages 792-802.

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