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Ensemble approaches to creating generalised meta-heuristic solvers - Project ID SOC0024

  • Full or part time
  • Application Deadline
    Applications accepted all year round
  • Self-Funded PhD Students Only
    Self-Funded PhD Students Only

Project Description

It well known that for practically any combinatorial optimisation problem that has been studied, different instances are best solved using different algorithms. One approach to address this is to create an ensemble of algorithms which collectively cover the potential instance-space (i.e. set of possible instances), and determine a mapping that projects instances and algorithms onto this space. Challenges exist in defining an appropriate space and in generating a diverse set of algorithms to cover the space. The PhD will focus on (1) the use of machine-learning and/or evolutionary methods to determine appropriate features to characterise the instance space, and (2) on the use of genetic programming/grammatical evolution to generate diverse meta-heuristics to cover the space, considering trade-offs between generalisation and specialisation.

Academic qualifications
A first degree (at least a 2.1) ideally in Computer Science with a good fundamental knowledge of biologically inspired search algorithms for combinatorial optimisation and machine learning techniques.

English language requirement
IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Essential attributes:
• Experience of fundamental programming techniques such as Java, C++, Python
• Competent in data analysis and basic statistics
• Knowledge of bio-inspired search methods and machine-learning technqiues
• Good written and oral communication skills
• Strong motivation, with evidence of independent research skills relevant to the project
• Good time management

Desirable attributes:
Experience with R, LaTeX, Linux and eclipse would be beneficial.

Edinburgh Napier University is committed to promoting equality and diversity in our staff and student community

Funding Notes

This an unfunded position.


Hart, E., & Sim, K. (2018). On Constructing Ensembles for Combinatorial Optimisation. Evolutionary Computation, 26(1), 67-87.

Hart, E., Sim, K., Gardiner, B., Kamimura, K.: A hybrid method for feature construction and selection to improve wind-damage prediction in the forestry sector. In: GECCO. pp. 1121-1128. ACM (2017)

Sim, K., Hart, E., Paechter, B.: A lifelong learning hyper-heuristic method for bin packing. Evolutionary Computation 23(1), 37{67 (2015)

Related Subjects

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