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Grant Dick imageBSc(Hons), PhD(Otago)
Senior Lecturer

Room 3.46, Otago Business School
Tel +64 3 479 8180
Email grant.dick@otago.ac.nz

Background and interests

Associate Professor Grant Dick is a member of the 100-level teaching group and has a background in Information Systems development.

Outside of teaching, his research interests include: Computational Intelligence methods, in particular evolutionary computation; Adaptive business intelligence; Multimodal and multi-objective problem solving; Theoretical population genetics; Evolving systems, particularly the role of population structure in speciation.

Grant is the recipient of a teaching award.

Research

Grant's overall research goal is to discover intelligent methods to solve difficult real-world problems. Broadly speaking, he is interested in computational intelligence methods and their application to scheduling, optimisation, data mining and multi-objective problem solving.

His primary research interest is in computational intelligence, which attempts to mimic problem solving techniques found in natural systems to solve difficult real-world problems. Computational intelligence methods are often able to reveal solutions to problems where “traditional” methods have previously failed. They are often useful in environments where desirable outcomes are constantly changing, or when complete descriptions of the desired solution are difficult to obtain.

Background

His PhD thesis explored the use of computational intelligence for multimodal problem solving. The techniques developed in his thesis are applicable to problems that possess potentially many equally-viable solutions. Examples of my work have appeared in internationally-respected journals, such as IEEE Transactions of Evolutionary Computation, Theoretical Population Biology and Soft Computing.

Potential collaborations

  • Scheduling and dispatch problems, particularly in dynamic or constrained environments
  • Applying computational intelligence techniques to discover anomalous behaviours in customers, patients, or workers
  • Optimisation of any problems with multiple conflicting goals (e.g. Cost vs. Time)
  • Prediction and forecasting

Papers

  • COMP101 Foundations of Information Systems

Supervision

Currently supervising:

  • Paul Williams
  • Caitlin Owen

Currently co-supervising:

  • Harry Peyhani
  • Aladdin Shamoug
  • Adriaan Lotter

Publications

Dick, G. (2024). An ensemble learning interpretation of geometric semantic genetic programming. Genetic Programming & Evolvable Machines, 25, 9. doi: 10.1007/s10710-024-09482-6

Shamoug, A., Cranefield, S., & Dick, G. (2023). SEmHuS: A semantically embedded humanitarian space. Journal of International Humanitarian Action, 8(3). doi: 10.1186/s41018-023-00135-4

Owen, C. A., Dick, G., & Whigham, P. A. (2023). Using decomposed error for reproducing implicit understanding of algorithms. Evolutionary Computation. Advance online publication. doi: 10.1162/evco_a_00321

Owen, C. A., Dick, G., & Whigham, P. A. (2022). Towards explainable AutoML using error decomposition. Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 13728). (pp. 177-190). Cham, Switzerland: Springer. doi: 10.1007/978-3-031-22695-3_32

Owen, C. A., Dick, G., & Whigham, P. A. (2022). Standardization and data augmentation in genetic programming. IEEE Transactions on Evolutionary Computation, 26(6), 1596-1608. doi: 10.1109/TEVC.2022.3160414

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