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Grant Dick

BSc (Hons), PhD(Otago)

Position
Senior Lecturer
Room
9.08, Commerce Building
Phone
+64 3 479 8180
Email
grant.dick@otago.ac.nz
Recipient of
Teaching Award
Co-supervising
Harry Peyhani, Paul Williams, Aladdin Shamoug
Papers
2017 S2: COMP101, INFO204
2018 SS: COMP101
2018 S1: 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

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. Advance online publication. 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

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. (2017). Revisiting interval arithmetic for regression problems in genetic programming. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO) Companion. (pp. 129-130). New York, NY: ACM. doi: 10.1145/3067695.3076107

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

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. Advance online publication. 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. 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. (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., & 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

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. (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

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

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. (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., & 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. (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. (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

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

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

Dick, G. (2012). Niche allocation in spatially-structured evolutionary algorithms with gradients. Proceedings of the Congress on Evolutionary Computation (CEC). doi: 10.1109/CEC.2012.6256542

Dick, G. (2010). Automatic identification of the niche radius using spatially-structured clearing methods. Proceedings of the IEEE Congress on Evolutionary Computation (CEC). (pp. 1264-1271). IEEE. doi: 10.1109/CEC.2010.5586085

Dick, G. (2010). The utility of scale factor adaptation in differential evolution. Proceedings of the IEEE Congress on Evolutionary Computation (CEC). (pp. 4355-4362). IEEE. doi: 10.1109/CEC.2010.5586480

Whigham, P. A., & Dick, G. (2008). Exploring the use of ancestry as a unified network model of finite population evolution. Proceedings of the IEEE Congress on Evolutionary Computation. (pp. 3735-3741). Los Alamitos, CA: IEEE Computer Society. [Full Paper]

Dick, G. (2007). The emergence and distribution of species in a gradient-based spatially-structured evolutionary algorithm. In P. A. Whigham (Ed.), Proceedings of the 19th Annual Colloquium of the Spatial Information Research Centre. (pp. 99-110). Dunedin, New Zealand: SIRC, University of Otago. [Full Paper]

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