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Grant Dick

BSc (Hons), PhD(Otago)

Position
Senior Lecturer
Room
9.08, Otago Business School Bldg
Phone
+64 3 479 8180
Email
grant.dick@otago.ac.nz
Recipient of
Teaching Award
Supervising
Paul Williams, Caitlin Owen
Co-supervising
Harry Peyhani, Aladdin Shamoug, Adriaan Lotter
Papers
2019 SS: COMP101
2019 S1: BSNS115, COMP101, COMP210, FORS201
2019 S2: COMP101
Research group
Data Science

About

Dr 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.

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Research

Aim

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

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Publications

Shamoug, A., Cranefield, S., & Dick, G. (2018). Information retrieval for humanitarian crises via a semantically classified word embedding. In K. Stock & D. Bunker (Eds.), Proceedings of the Information Systems for Crisis Response and Management Asia Pacific 2018 Conference: Innovating for Resilience. (pp. 132-144). Wellington, New Zealand: Massey University. [Full Paper]

Chugh, M., Whigham, P. A., & Dick, G. (2018). Stability of word embeddings using Word2Vec. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 812-818). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_73

Owen, C. A., Dick, G., & Whigham, P. A. (2018). Feature standardisation in symbolic regression. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 565-576). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_52

Dick, G., Owen, C. A., & Whigham, P. A. (2018). Evolving bagging ensembles using a spatially-structured niching method. Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 418-425). New York, NY: ACM. doi: 10.1145/3205455.3205642

Whigham, P. A., Chugh, M., & Dick, G. (2018). Measuring language complexity using word embeddings. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 843-854). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_76

Chapter in Book - Research

Dick, G., & Whigham, P. A. (2008). A weighted local sharing technique for multimodal optimisation. In X. Li & et al (Eds.), Simulated evolution and learning: Lecture notes in computer science (Vol. 5361). (pp. 452-461). Berlin, Germany: Springer.

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Journal - Research Article

Whigham, P. A., Dick, G., & Maclaurin, J. (2017). Just because it works: A response to comments on "On the mapping of genotype to phenotype in evolutionary algorithms". Genetic Programming & Evolvable Machines, 18(3), 399-405. doi: 10.1007/s10710-017-9289-9

Whigham, P. A., Dick, G., & Maclaurin, J. (2017). On the mapping of genotype to phenotype in evolutionary algorithms. Genetic Programming & Evolvable Machines, 18(3), 353-361. doi: 10.1007/s10710-017-9288-x

Whigham, P. A., Dick, G., & Parry, M. (2016). Network rewiring dynamics with convergence towards a star network. Proceedings of the Royal Society A, 472(2194), 20160236. doi: 10.1098/rspa.2016.0236

Dick, G., & Whigham, P. A. (2011). Weighted local sharing and local clearing for multimodal optimisation. Soft Computing, 15, 1707-1721. doi: 10.1007/s00500-010-0612-0

Whigham, P. A., & Dick, G. (2010). Implicitly controlling bloat in genetic programming. IEEE Transactions on Evolutionary Computation, 14(2), 173-190. doi: 10.1109/tevc.2009.2027314

Whigham, P. A., & Dick, G. (2008). Evolutionary dynamics for the spatial Moran process. Genetic Programming & Evolvable Machines, 9(2), 157-170. doi: 10.1007/s10710-007-9046-6

Dick, G., & Whigham, P. (2008). Spatially-structured sharing technique for multimodal problems. Journal of Computer Science & Technology, 23(1), 64-76.

Whigham, P. A., Dick, G. C., & Spencer, H. G. (2008). Genetic drift on networks: Ploidy and the time to fixation. Theoretical Population Biology, 74(4), 283-290. doi: 10.1016/j.tpb.2008.08.004

Whigham, P. A., Dick, G., & Recknagel, F. (2006). Exploring seasonal patterns using process modelling and evolutionary computation. Ecological Modelling, 195, 146-152.

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Conference Contribution - Published proceedings: Full paper

Shamoug, A., Cranefield, S., & Dick, G. (2018). Information retrieval for humanitarian crises via a semantically classified word embedding. In K. Stock & D. Bunker (Eds.), Proceedings of the Information Systems for Crisis Response and Management Asia Pacific 2018 Conference: Innovating for Resilience. (pp. 132-144). Wellington, New Zealand: Massey University. [Full Paper]

Whigham, P. A., Chugh, M., & Dick, G. (2018). Measuring language complexity using word embeddings. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 843-854). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_76

Dick, G., Owen, C. A., & Whigham, P. A. (2018). Evolving bagging ensembles using a spatially-structured niching method. Proceedings of the Genetic and Evolutionary Computation Conference. (pp. 418-425). New York, NY: ACM. doi: 10.1145/3205455.3205642

Owen, C. A., Dick, G., & Whigham, P. A. (2018). Feature standardisation in symbolic regression. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artifical intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 565-576). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_52

Chugh, M., Whigham, P. A., & Dick, G. (2018). Stability of word embeddings using Word2Vec. In T. Mitrovic, B. Xue & X. Li (Eds.), Advances in artificial intelligence: Lecture notes in artificial intelligence (Vol. 11320). (pp. 812-818). Cham, Switzerland: Springer. doi: 10.1007/978-3-030-03991-2_73

Dick, G. (2017). Sensitivity-like analysis for feature selection in genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). (pp. 401-408). New York, NY: ACM. doi: 10.1145/3071178.3071338

Dick, G., Rimoni, A. P., & Whigham, P. A. (2015). A re-examination of the use of genetic programming on the oral bioavailability problem. In S. Silva (Ed.), Proceedings of the 2015 on Genetic and Evolutionary Computation Conference (GECCO). (pp. 1015-1022). New York: ACM. doi: 10.1145/2739480.2754771

Dick, G. (2015). Improving geometric semantic genetic programming with safe tree initialisation. In P. Machado, M. I. Heywood, J. McDermott, M. Castelli, P. García-Sánchez, P. Burelli, … K. Sim (Eds.), Genetic programming: Lecture notes in computer science (Vol. 9025). (pp. 28-40). Springer. doi: 10.1007/978-3-319-16501-1_3

Whigham, P. A., Dick, G., Maclaurin, J., & Owen, C. A. (2015). Examining the "best of both worlds" of grammatical evolution. Proceedings of the Genetic and Evolutionary Computation (GECCO) Conference. (pp. 1111-1118). New York: ACM. doi: 10.1145/2739480.2754784

Dick, G., & Yao, X. (2014). Model representation and cooperative coevolution for finite-state machine evolution. Proceedings of the Congress on Evolutionary Computation (CEC). (pp. 2700-2707). IEEE. doi: 10.1109/cec.2014.6900622

Dick, G. (2014). Bloat and generalisation in symbolic regression. In G. Dick, W. N. Browne, P. Whigham, M. Zhang, L. T. Bui, H. Ishibuchi, … K. Tang (Eds.), Simulated evolution and learning: Lecture notes in computer science (Vol. 8886). (pp. 491-502). Cham, Switerzland: Springer. doi: 10.1007/978-3-319-13563-2

Dick, G. (2013). A true finite-state baseline for Tartarus. In C. Blum (Ed.), Proceedings of the Fifteenth Annual Conference on Genetic and Evolutionary Computation (GECCO). (pp. 183-190). New York: ACM. doi: 10.1145/2463372.2463400

Dick, G., & Whigham, P. A. (2013). Controlling bloat through parsimonious elitist replacement and spatial structure. In K. Krawiec, A. Moraglio, T. Hu, A. Ş. Etaner-Uyar & B. Hu (Eds.), Genetic programming: Lecture notes in computer science (Vol. 7831). (pp. 13-24). Berlin, Germany: Springer. doi: 10.1007/978-3-642-37207-0_2

Dick, G. (2013). An effective parse tree representation for Tartarus. In C. Blum (Ed.), Proceedings of the Fifteenth Annual Conference on Genetic and Evolutionary Computation (GECCO). (pp. 909-916). New York: ACM. doi: 10.1145/2463372.2463497

Whigham, P. A., Dick, G., Wright, A., & Spencer, H. G. (2013). Structured populations and the maintenance of sex. In L. Vanneschi, W. S. Bush & M. Giacobini (Eds.), Evolutionary computation, machine learning and data mining in bioinformatics: Lecture notes in computer science (Vol. 7833). (pp. 56-67). Berlin, Germany: Springer. doi: 10.1007/978-3-642-37189-9_6

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