Multi-criteria Automatic Algorithm Configuration under Streaming Problem Instances

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This is one of two research projects developing new tools for automated algorithm selection and configuration. The University of Manchester is the home institution for this project. To view the Melbourne-based partner project, click here.

Many critical problems in logistics, manufacturing, healthcare and other fields are solved by optimisation and machine learning algorithms. Thanks to advances in automatic configuration tools, we are now able to automatically tune the parameters of these algorithms for new problems with minimal human effort.

Unfortunately, these tools are designed to tune algorithms according to a single criterion and assume that the characteristics of a problem do not change over time. In the real world, however, the users of such algorithms often face conflicting criteria, such as the time required to solve the problem versus the expected quality of the solution returned by the algorithm.

Moreover, it is often the case that similar problems must be solved regularly (for example daily) in the case of a parcel delivery service, a manufacturing plant processing orders in daily batches or the daily planning of operating theatres in hospitals.

In those cases, the characteristics of the daily instances of the problem may evolve over time due to economical, societal and technological changes.

This project aims to extend the capabilities of automatic configuration tools to handle multiple conflicting criteria and adapt to such changes in the problem characteristics.

For this purpose, the teams at Manchester and Melbourne will join their expertise in the automatic configuration of algorithms and instance space analysis. The result of this project will be more powerful tools for tuning and deploying the critical algorithms that our modern world relies on so that they can better adapt to changes in the problems being solved and let users decide the most appropriate trade-off among conflicting criteria.

Project goals

The goals of this project are to:

  1. Develop automated configuration (AC) methods capable of generating a portfolio of configurations according to multiple criteria when facing a stream of problem instances.
  2. Develop AC methods based on an instance space analysis that permits parameter tuning based on knowledge of where an instance lies in the instance space, how its characteristics necessitate re-configuration of the algorithm, and why.
  3. Augment test suites to include more strategically chosen test instances to improve explainability.

The main research questions are:

  1. How to handle multiple criteria in AC methods and produce a Pareto-optimal portfolio of configurations?
  2. What techniques from existing AC methods are applicable to the streaming scenario and what new methods need to be developed?
  3. How to generate and analyse problem instances that simulate various streaming scenarios in which instance features may evolve slowly or more rapidly?

Supervision team

The University of Manchester: Associate Professor Manuel López-Ibáñez, Professor Julia Handl

The University of Melbourne: Professor Kate Smith-Miles, Doctor Mario Andrés Muñoz-Acosta

*Click on the researcher's name above to learn more about their publication and grant successes.

Who we are looking for

We are seeking a PhD candidate with the following skills:

  • Demonstrated experience in the field of computer science or mathematics and statistics.
  • Demonstrated experience with mathematical optimisation/machine learning.
  • Demonstrated ability to work independently and as part of a team.
  • Demonstrated ability to write research reports or other publications to a publishable standard (even if not published to date).
  • Excellent written and oral communication skills.
  • Demonstrated time management, project management and organisational skills, and ability to work to priorities.
  • Demonstrated problem-solving abilities.

Further details

The PhD candidate will benefit from the combined expertise of the project supervisors, and the embedding into two research environments. Associate Professor Manuel López-Ibáñez (PI) is a worldwide expert on the automated configuration of algorithms and Professor Julia Handl is an expert in multi-objective optimisation and machine learning, including for streaming data. The team led by Melbourne Laureate Professor Kate Smith-Miles, will contribute by extending unique tools for Instance Space Analysis to analyse and visualise the relationship between instance features and configuration criteria as well as how a stream of instances evolves over time.

This PhD project will be based at the University of Manchester with a minimum 12-month stay at the University of Melbourne.

The candidate will be enrolled in the PhD program at the Alliance Manchester Business School at the University of Manchester and in the PhD program at the School of Mathematics and Statistics at the University of Melbourne.

The supervisory team at Manchester are both members of the Institute for Data Science and Artificial Intelligence (IDSAI) of the University of Manchester and Turing Fellows of the Alan Turing Institute. Both institutes provide access to a large network of researchers within the University of Manchester and the UK. In particular, colleagues at IDSAI working on mathematical optimisation and machine learning algorithms for manufacturing, logistics and healthcare problems will be interested in learning more about the capabilities provided by the methodology and MATILDA tool developed by Melbourne.

To apply for this joint PhD opportunity, and to view the entry requirements, visit How to apply.

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