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Grant Dick

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

Grant is currently on Research and Study Leave, returning in November
Email grant.dick@otago.ac.nz

Background and interests

 

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.

Grant is the recipient of a teaching award.

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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

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Papers

Supervision

Currently supervising:

  • Paul Williams
  • Caitlin Owen

Currently co-supervising:

  • Harry Peyhani
  • Aladdin Shamoug
  • Adriaan Lotter

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Publications

Dick, G., Owen, C. A., & Whigham, P. A. (2020). Feature standardisation and coefficient optimisation for effective symbolic regression. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). (pp. 306-314). ACM. doi: 10.1145/3377930.3390237

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]

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). 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., & 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., & 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

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

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., Owen, C. A., & Whigham, P. A. (2020). Feature standardisation and coefficient optimisation for effective symbolic regression. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). (pp. 306-314). ACM. doi: 10.1145/3377930.3390237

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. (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). 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

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

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]

Dick, G., & Whigham, P. A. (2006). Spatially-structured evolutionary algorithms and sharing: Do they mix? In T.-D. Wang, X. Li, S.-H. Chen, X. Wang, H. Abbass, H. Iba, … X. Yao (Eds.), Proceedings of the 6th International Conference on Simulated Evolution and Learning. (pp. 457-464). Berlin, Germany: Springer. [Full Paper]

Whigham, P. A., & Dick, G. (2006). Evolutionary dynamics on graphs: The Moran Process. In T.-D. Wang, X. Li, S.-H. Chen, X. Wang, H. Abbass, H. Iba, … X. Yao (Eds.), Proceedings of the 6th International Conference on Simulated Evolution and Learning (LNCS 4247). (pp. 1-8). Berlin, Germany: Springer. [Full Paper]

Whigham, P., & Dick, G. (2006). How does space alter the formulation of evolutionary models? In P. A. Whigham (Ed.), Proceedings of the 18th Annual Colloquium of the Spatial Information Research Centre. (pp. 79-84). Dunedin, New Zealand: SIRC, University of Otago. [Full Paper]

Dick, G. (2006). Evolutionary multiobjective optimisation through spatially-structured non-dominated sorting: A preliminary study. In P. A. Whigham (Ed.), Proceedings of the 18th Annual Colloquium of the Spatial Information Research Centre. (pp. 87-96). Dunedin, New Zealand: SIRC, University of Otago. [Full Paper]

Whigham, P. A., & Dick, G. (2006). GP and bloat: Absorbing boundaries and spatial structures. In R. I. McKay, N. X. Hoai & P. T. Long (Eds.), Proceedings of the Third Asian-Pacific Workshop on Genetic Programming. (pp. 1-12). Hanoi, Vietnam: Military Technical Academy. [Full Paper]

Dick, G., & Whigham, P. A. (2006). Multimodal optimisation with structured populations and local environments. In T.-D. Wang, X. Li, S.-H. Chen, X. Wang, H. Abbass, H. Iba, … X. Yao (Eds.), Proceedings of the 6th International Conference on Simulated Evolution and Learning. (pp. 505-512). Berlin, Germany: Springer. [Full Paper]

Dick, G., & Whigham, P. A. (2005). Discovering population structures with extreme fixation rates via evolutionary search. In P. A. Wigham (Ed.), Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre. (pp. 175-179). [Full Paper]

Whigham, P. A., & Dick, G. (2005). Fixation of neural alleles in spatially structures populations via genetic drift: Describing the spatial structure of faster-than-panmictic configurations. In P. A. Wigham (Ed.), Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre. (pp. 81-90). [Full Paper]

Dick, G. (2005). A comparison of localised and global niching methods. In P. A. Wigham (Ed.), Proceedings of the 17th Annual Colloquium of the Spatial Information Research Centre. (pp. 91-101). [Full Paper]

Dick, G., & Whigham, P. (2005). The behaviour of genetic drift in a spatially-structured evolutionary algorithm. Proceedings of the IEEE Congress on Evolutionary Computation. 2, (pp. 1855-1860). Piscataway, NJ, USA: IEEE Press. [Full Paper]

Dick, G. (2004). A general framework for describing spatially-structured populations in evolutionary computation. In P. A. Wigham & B. R. McLennan (Eds.), Proceedings of the 16th Annual Colloquium of the Spatial Information Research Centre. (pp. 117-126). [Full Paper]

Dick, G. (2004). An empirical investigation into correlation functions in a spatially-dispersed evolutionary algorithm. In P. A. Wigham & B. R. McLennan (Eds.), Proceedings of the 16th Annual Colloquium of the Spatial Information Research Centre. (pp. 23-33). [Full Paper]

Jang, D. K., Whigham, P. A., & Dick, G. (2004). On evolving fixed pattern strategies for Iterated Prisoner's Dilemma. Proceedings of the 27th Australasian Computer Science Conference. (pp. 241-248). [Full Paper]

Dick, G. (2003). An explicit spatial model for niching in genetic algorithms. In P. A. Wigham & A. Moore (Eds.), Proceedings of the 15th Annual Colloquium of the Spatial Information Research Centre. (pp. 151-157). [Full Paper]

Middlemiss, M., & Dick, G. (2003). Weighted feature extraction using a genetic algorithm for intrusion detection. In A. Abraham, M. Koppen & K. Franke (Eds.), Proceedings of the Congress on Evolutionary Computation. (pp. 1669-1675). [Full Paper]

Whigham, P. A., & Dick, G. (2003). A Voromoi-based distributed genetic algorithm. In P. A. Wigham & A. Moore (Eds.), Proceedings of the 15th Annual Colloquium of the Spatial Information Research Centre. (pp. 133-138). [Full Paper]

Middlemiss, M., & Dick, G. (2003). Feature selection of intrusion detection data using a Hybrid Genetic Algorithm/KNN Approach. In A. Abraham, M. Koppen & K. Franke (Eds.), Design and Application of Hybrid Intelligent Systems. (pp. 519-527). [Full Paper]

Dick, G. (2003). The spatially-dispersed genetic algorithm: An explicit spatial population structure for gas. In A. Abraham, M. Koppen & K. Franke (Eds.), Proceedings of the Congress on Evolutionary Computation. (pp. 2455-2461). [Full Paper]

Whigham, P., & Dick, G. C. (2003). A Voronoi-based distributed genetic algorithm. In P. A. Whigham & A. Moore (Eds.), Proceedings of the 15th Annual Colloquium of the Spatial Information Research Centre. (pp. 133-138). Dunedin: University of Otago. [Full Paper]

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