Machine Learning and Hyper-heuristics

Hyper-heuristics (HH) have been successfully used to solve combinatorial problems from different problem domains, such as vehicle routing, bin packing and constraint satisfaction problems. HH were introduced under the acclaim that they are applicable to different problems without modification, in an attempt to produce more generalizable techniques that could be applied without the time consuming task of tailoring and tuning an algorithm specifically for the problem. Results on different problem domains indicate that HH suffer from the same issues as other biologically inspired search methods, namely that increasing performance on one problem domain, or a subset of problem instances from a domain has a negative effect on the performance on other problem domains, or different problem instances.

The task of tuning an algorithm is a complex task that often requires expert input, and a large amount of time and resources. Once tuned this information is typically disregarded and the algorithm needs retuned when faced with a different problem. The project will use machine learning to identify correlations between algorithm performance and problem attributes in an attempt to identify the best approach to be used for solving a particular problem. A background in biologically inspired search algorithms is essential as is a good knowledge of machine learning techniques and combinatorial search problems.

Academic qualifications

A second class honour degree or equivalent qualification in Computer Science or a closely related discipline.problems.

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, Dr. Kevin Sim (K.Sim@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.