Grant Dick, Our people, Department of Information Science

BSc(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

Owen, C. A., Dick, G., & Whigham, P. A. (2024). Revisiting bagging for stochastic algorithms. In M. Gong, Y. Song, Y. S. Koh, W. Xiang & D. Wang (Eds.), Advances in Artificial Intelligence: Proceedings of the 37th Australasian Joint Conference on Artificial Intelligence (Part II): Lecture notes in artificial intelligence (Vol. 15443). (pp. 162-173). Singapore: Springer. doi: 10.1007/978-981-96-0351-0 Conference Contribution - Published proceedings: Full paper

Dick, G., & Owen, C. A. (2024). Characterising the double descent of symbolic regression. Proceedings of the Genetic and Evolutionary Computation Conference (GECCO). (pp. 2050-2057). New York, NY: ACM. doi: 10.1145/3638530.3664176 Conference Contribution - Published proceedings: Full paper

Dick, G. (2024, February). AI and national security decision making. Verbal presentation at the Otago National Security School, Dunedin, New Zealand. Conference Contribution - Verbal presentation and other Conference outputs

de Franca, F. O., Virgolin, M., Kommenda, M., Majumder, M. S., Cranmer, M., Espada, G., … Dick, G., … La Cava, W. G. (2024). SRBench++: Principled benchmarking of symbolic regression with domain-expert interpretation. IEEE Transactions on Evolutionary Computation. Advance online publication. doi: 10.1109/TEVC.2024.3423681 Journal - Research Article

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 Journal - Research Article

2024

Journal - Research Article

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

Conference Contribution - Published proceedings: Full paper

Conference Contribution - Verbal presentation and other Conference outputs

Dick, G. (2024, February). AI and national security decision making. Verbal presentation at the Otago National Security School, Dunedin, New Zealand.

2023

Journal - Research Article

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

2022

Journal - Research Article

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

Conference Contribution - Published proceedings: Full paper

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

2020

Journal - Research Article

Owen, C. A., Dick, G., & Whigham, P. A. (2020). Characterizing genetic programming error through extended bias and variance decomposition. IEEE Transactions on Evolutionary Computation, 24(6), 1164-1176. doi: 10.1109/tevc.2020.2990626

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 & Evolutionary Computation Conference (GECCO). (pp. 306-314). New York, NY: ACM. doi: 10.1145/3377930.3390237

2018

Conference Contribution - Published proceedings: 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

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

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]

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

2017

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

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

Conference Contribution - Published proceedings: Abstract

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

2016

Journal - Research Article

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

2015

Conference Contribution - Published proceedings: Full paper

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., 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

Conference Contribution - Published proceedings: Abstract

Rimoni, A., & Dick, G. (2015). Is genetic programming good at symbolic regression? In C. Matthewson, M. Taumoepeau, I. Iati, M. Inder, C. Pau & R. Richards (Eds.), Proceedings of the Pacific Postgraduate Symposium: Pacific Voices XII. (pp. 35). Dunedin, New Zealand: Pacific Islands Centre, University of Otago. [Abstract]

2014

Conference Contribution - Published proceedings: Full paper

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

Conference Contribution - Edited volume of conference proceedings

Dick, G., Browne, W. N., Whigham, P., Zhang, M., Bui, L. T., Ishibuchi, H., … Tang, K. (Eds.). (2014). Simulated evolution and learning: Lecture notes in computer science (Vol. 8886). Cham: Springer. 862p. doi: 10.1007/978-3-319-13563-2

2013

Conference Contribution - Published proceedings: Full paper

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

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

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

2012

Conference Contribution - Published proceedings: Full paper

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

2011

Journal - Research Article

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

2010

Journal - Research Article

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

Conference Contribution - Published proceedings: Full paper

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

2008

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.

Journal - Research Article

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

Conference Contribution - Published proceedings: Full paper

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]

Awarded Doctoral Degree

Dick, G. (2008). Spatially-structured niching methods for evolutionary algorithms (PhD). University of Otago, Dunedin, New Zealand. 139p.

2007

Conference Contribution - Published proceedings: 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]

2006

Journal - Research Article

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

Conference Contribution - Published proceedings: 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). 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. 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. (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., & 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]

2005

Conference Contribution - Published proceedings: 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]

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]

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., & 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]

2004

Conference Contribution - Published proceedings: 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]

2003

Conference Contribution - Published proceedings: 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]

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]

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]

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]

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]

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]

Conference Contribution - Poster Presentation (not in published proceedings)

Dick, G. (2003, July). The spatially-dispersed genetic algorithm. Poster session presented at the Genetic and Evolutionary Computation Conference, Chicage, IL, USA.

2002

Conference Contribution - Published proceedings: Full paper

Dick, G. C., & Whigham, P. (2002). Spatially-constrained selection in evolutionary computation. In R. Sarker, B. McKay, M. Gen & A. Namatame (Eds.), Proceedings of the 6th Australasia-Japan Joint Workshop on Intelligent and Evolutionary Systems. (pp. 93-101). Japan: Ashikaga Institute of Technology. [Full Paper]

Conference Contribution - Published proceedings: Abstract

Whigham, P., & Dick, G. C. (2002). A study of spatial distribution and evolution. In P. A. Whigham (Ed.), The 14th Annual Colloquium of the Spatial Information Research Centre. (pp. 157-166). Dunedin: Otago University Print. [Abstract]