Continual Learning in Black-Box Optimisation

Optimisation problems are ubiquitous across many sectors. In a typical scenario, instances arrive in a continual stream and a solution needs to be quickly produced. Meta-heuristic search techniques have proved useful in providing high-quality solutions, but it is challenging to select the correct solver for a particular instance and/or tune it to optimise performance. If the characteristics of instances change over time, it is also possible that at some future point, instances are sufficiently novel that there is no appropriate solver known or the selector is incapable of choosing the best algorithm.

This project will focus on one or more aspects of tackling this issue; for instance developing novel algorithm-selection methods that are capable of selecting the most appropriate method; using algorithm-generation methods (e.g. genetic programming) to generate or tune algorithms to work well on instances that occur in novel regions of the instance space; developing methods that are capable of learning from experience, i.e. continually improving selection methods or generation methods over time as knowledge is learned from solving past instances. The project is likely to mix techniques from meta-heuristic optimisation, automated algorithm generation and machine-learning, particularly borrowing ideas from the transfer learning or continual learning literature.

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements in Computer Science, Operations Research, or Mathematics (assuming a good level of programming skills).

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Professor Emma Hart (e.hart@napier.ac.uk) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• List of references

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here.

Download a copy of the project details here.