Mengjie Zhang, Evolutionary Transfer Learning

Evolutionary Scheduling and Combinatorial Optimisation

Scheduling and Combinatorial Optimization is an active research area in both Artificial Intelligence and Operations Research due to its applicability and interesting computational aspects. Scheduling and combinatorial optimization problems include a wide range of combinatorial optimization and search problems in which the task is to accommodate a set of entities such as events, items, tasks, projects, activities, people and vehicles into a pattern of time-space so that the available resources are utilized as efficiently as possible and the additional constraints are satisfied.

Evolutionary methods refer to a range of computational approaches that are often inspired by processes that occur in nature. Examples of evolutionary methods include genetic algorithm, genetic programming, evolutionary strategies, ant colony optimisation, particle swarm optimisation, evolutionary based hyper-heuristics, memetic algorithms.

Evolutionary techniques are suitable for these problems since they are highly flexible in terms of handling constraints, dynamic changes and multiple conflicting objectives. Evolutionary methods have been applied to a number of problems including optimization, search and design with considerable success. However, there are still many issues to be investigated in this area.

This project aims to develop new algorithms for evolutionary scheduling and combinatorial optimisation problems. Specifically, we would like to develop new algorithms for

  • Reusability of evolved heuristic rules for static and dynamic job shop scheduling problems
  • Scalability of the evolved heuristic rules for static and dynamic job shop scheduling problems
  • Transfer learning including domain generalisation and domain adaptation in scheduling and web service composition
  • Interpretation and comprehensibility of the evolved heuristic rules for scheduling and web service composition, including online program simplification
  • Feature selection and construction in in scheduling and web service composition
  • New EC algorithms for flexible job shop scheduling, routing, resource allocation in grid/cloud computing
A strong background in Java/C/C++ programming and a basic background in Artificial Intelligence and statistics are required. A good background in machine learning, and operations research is desired (COMP307, COMP361).

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