Dr Lee Christie's Outputs (22)

Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics. (2024)
Thesis
FYVIE, M. 2024. Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics. Robert Gordon University, PhD thesis. Hosted on OpenAIR [online]. Available from: https://doi.org/10.48526/rgu-wt-2565263

Evolutionary algorithms (EAs) are the principal focus of research study in Evolutionary Computing (EC). In EC, naturally occurring processes designed to drive success in nature are simulated for a similar purpose in numerical optimisation. Such proce... Read More about Explainability of non-deterministic solvers: explanatory feature generation from the data mining of the search trajectories of population-based metaheuristics..

Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. (2024)
Journal Article
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J.A.W. 2024. Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms. IEEE transactions on intelligent transportation systems, [online], 25(9), pages 10790-10801. Available from: https://doi.org/10.1109/TITS.2024.3374550

The past five years have seen rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. While self-driving technology is still being perfected, public transp... Read More about Exploring representations for optimising connected autonomous vehicle routes in multi-modal transport networks using evolutionary algorithms..

Towards explainable metaheuristics: feature extraction from trajectory mining. (2023)
Journal Article
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., BROWNLEE, A.E.I. and SINGH, M. [2023]. Towards explainable metaheuristics: feature extraction from trajectory mining. Expert systems [online], Early View. Available from: https://doi.org/10.1111/exsy.13494

Explaining the decisions made by population-based metaheuristics can often be considered difficult due to the stochastic nature of the mechanisms employed by these optimisation methods. As industries continue to adopt these methods in areas that incr... Read More about Towards explainable metaheuristics: feature extraction from trajectory mining..

Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms. (2021)
Presentation / Conference Contribution
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J. 2021. Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms. Presented at 2021 Genetic and evolutionary computation conference (GECCO 2021), 10-14 July 2021, [virtual conference].

The past five years have seen a rapid development of plans and test pilots aimed at introducing connected and autonomous vehicles (CAVs) in public transport systems around the world. Using a real-world scenario from the Leeds Metropolitan Area as a c... Read More about Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms..

Multi-objective evolutionary design of antibiotic treatments. (2019)
Journal Article
OCHOA, G., CHRISTIE, L.A., BROWNLEE, A.E. and HOYLE, A. 2020. Multi-objective evolutionary design of antibiotic treatments. Artificial intelligence in medicine [online], 102, article number 101759. Available from: https://doi.org/10.1016/j.artmed.2019.101759

Antibiotic resistance is one of the major challenges we face in modern times. Antibiotic use, especially their overuse, is the single most important driver of antibiotic resistance. Efforts have been made to reduce unnecessary drug prescriptions, but... Read More about Multi-objective evolutionary design of antibiotic treatments..

Investigating benchmark correlations when comparing algorithms with parameter tuning: detailed experiments and results. (2018)
Report
CHRISTIE, L.A., BROWNLEE, A.E.I. and WOODWARD, J.R. 2018. Investigating benchmark correlations when comparing algorithms with parameter tuning: detailed experiments and results. Stirling: University of Stirling [online]. Available from: http://hdl.handle.net/1893/26956

Benchmarks are important to demonstrate the utility of optimisation algorithms, but there is controversy about the practice of benchmarking; we could select instances that present our algorithm favourably, and dismiss those on which our algorithm und... Read More about Investigating benchmark correlations when comparing algorithms with parameter tuning: detailed experiments and results..

The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance. (2016)
Thesis
CHRISTIE, L.A. 2016. The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance. Robert Gordon University, PhD thesis.

Optimisation heuristics rely on implicit or explicit assumptions about the structure of the black-box fitness function they optimise. A review of the literature shows that understanding of structure and linkage is helpful to the design and analysis o... Read More about The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance..

Walsh families of all rank-invariant classes of 3-bit pseudo-Boolean functions. [Dataset] (2016)
Data
CHRISTIE, L.A. 2016. Walsh families of all rank-invariant classes of 3-bit pseudo-Boolean functions. [Dataset]

This dataset was compiled as part of the following PhD thesis: CHRISTIE, L.A. 2016. The role of Walsh structure and ordinal linkage in the optimisation of pseudo-Boolean functions under monotonicity invariance. Robert Gordon University, PhD thesis. H... Read More about Walsh families of all rank-invariant classes of 3-bit pseudo-Boolean functions. [Dataset].

Generating easy and hard problems using the proximate optimality principle. [Dataset] (2015)
Data
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. [Dataset]

These data were gathered to investigate the hypothesis that coherent functions will be easy and anti-coherent functions will be hard for a hillclimber. We generated 10 coherent functions for each length on bit-strings of length 6-100 and the same num... Read More about Generating easy and hard problems using the proximate optimality principle. [Dataset].

On the multi-objective optimization of wind farm cable layouts with regard to cost and robustness.
Presentation / Conference Contribution
CHRISTIE, L.A., SAHIN, A., OGUNSEMI, A., ZĂVOIANU, A.-C. and MCCALL, J.A.W. 2024. On the multi-objective optimization of wind farm cable layouts with regard to cost and robustness. In Affenzeller, M., Winkler, S.M., Kononova, A.V. et al. (eds). Parallel problem solving from nature (PPSN XVIII): proceedings of the 18th Parallel problem solving from nature international conference 2024 (PPSN 2024), 14-18 September 2024, Hagenberg, Austria. Lecture notes in computer science, 15151. Cham: Springer [online], pages 367-382. Available from: https://doi.org/10.1007/978-3-031-70085-9_23

Offshore wind farms (OWFs) have emerged as a vital component in the transition to renewable energy, especially for countries like the United Kingdom with abundant shallow coastal waters suitable for wind energy exploitation. As net-zero emissions tar... Read More about On the multi-objective optimization of wind farm cable layouts with regard to cost and robustness..

Cost and performance comparison of holistic solution approaches for complex supply chains on a novel linked problem benchmark.
Presentation / Conference Contribution
OGUNSEMI, A., MCCALL, J., ZAVOIANU, C. and CHRISTIE, L.A. 2024. Cost and performance comparison of holistic solution approaches for complex supply chains on a novel linked problem benchmark. In Proceedings of the Genetic and evolutionary computation conference 2024 (GECCO'24), 14-18 July 2024, Melbourne, Australia. New York: Association for Computing Machinery (ACM) [online], pages 1327- 1335. Available from: https://doi.org/10.1145/3638529.3654163

Modern supply chains are complex structures of interacting units exchanging goods and services. Business decisions made by individual units in the supply chain have knock-on effects on decisions made by successor units in the chain. Linked Optimisati... Read More about Cost and performance comparison of holistic solution approaches for complex supply chains on a novel linked problem benchmark..

Explaining a staff rostering problem by mining trajectory variance structures.
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A., ZĂVOIANU, A.-C., BROWNLEE, A.E.I. and AINSLIE, R. 2023. Explaining a staff rostering problem by mining trajectory variance structures. In Bramer, M. and Stahl, F. (eds.) Artificial intelligence XL: proceedings of the 43rd SGAI international conference on artificial intelligence (AI-2023), 12-14 December 2023, Cambridge, UK. Lecture notes in computer science, 14381. Cham: Springer [online], pages 275-290. Available from: https://doi.org/10.1007/978-3-031-47994-6_27

The use of Artificial Intelligence-driven solutions in domains involving end-user interaction and cooperation has been continually growing. This has also lead to an increasing need to communicate crucial information to end-users about algorithm behav... Read More about Explaining a staff rostering problem by mining trajectory variance structures..

Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis.
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2023. Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis. In GECCO’23 companion: proceedings of the 2023 Genetic and evolutionary computation conference companion, 15-19 July 2023, Lisbon, Portugal. New York: ACM [online], pages 1648-1656. Available from: https://doi.org/10.1145/3583133.3596353

In the field of Explainable AI, population-based search metaheuristics are of growing interest as they become more widely used in critical applications. The ability to relate key information regarding algorithm behaviour and drivers of solution quali... Read More about Explaining a staff rostering genetic algorithm using sensitivity analysis and trajectory analysis..

On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems.
Presentation / Conference Contribution
HAN, K., CHRISTIE, L.A., ZAVOIANU, A.-C. and MCCALL, J. 2022. On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems. In Moreno-Díaz, R., Pichler, F. and Quesada-Arencibia, A. (eds.) Computer aided systems theory: Eurocast 2022; revised selected papers from the 18th International conference on computer aided systems theory (Eurocast 2022), 20-25 February 2022, Las Palmas, Spain. Lecture notes in computer science, 13789. Cham: Springer [online], pages 104-111. Available from: https://doi.org/10.1007/978-3-031-25312-6_12

While self-driving technology is still being perfected, public transport authorities are increasingly interested in the ability to model and optimise the benefits of adding connected and autonomous vehicles (CAVs) to existing multi-modal transport sy... Read More about On discovering optimal trade-offs when introducing new routes in existing multi-modal public transport systems..

Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms.
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2021. Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. In Bramer, M. and Ellis, R (eds.) Artificial intelligence XXXVIII: proceedings of 41st British Computer Society's Specialist Group on Artificial Intelligence (SGAI) Artificial intelligence international conference 2021 (AI-2021) (SGAI-AI 2021), 14-16 December 2021, [virtual conference]. Lecture notes in computer science, 13101. Cham: Springer [online], pages 89-102. Available from: https://doi.org/10.1007/978-3-030-91100-3_7

The generation of explanations regarding decisions made by population-based meta-heuristics is often a difficult task due to the nature of the mechanisms employed by these approaches. With the increase in use of these methods for optimisation in indu... Read More about Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms..

Non-deterministic solvers and explainable AI through trajectory mining.
Presentation / Conference Contribution
FYVIE, M., MCCALL, J.A.W. and CHRISTIE, L.A. 2021. Non-deterministic solvers and explainable AI through trajectory mining. In Martin, K., Wiratunga, N. and Wijekoon, A. (eds.) SICSA XAI workshop 2021: proceedings of 2021 SICSA (Scottish Informatics and Computer Science Alliance) eXplainable artificial intelligence workshop (SICSA XAI 2021), 1st June 2021, [virtual conference]. CEUR workshop proceedings, 2894. Aachen: CEUR-WS [online], session 4, pages 75-78. Available from: http://ceur-ws.org/Vol-2894/poster2.pdf

Traditional methods of creating explanations from complex systems involving the use of AI have resulted in a wide variety of tools available to users to generate explanations regarding algorithm and network designs. This however has traditionally bee... Read More about Non-deterministic solvers and explainable AI through trajectory mining..

Decentralized combinatorial optimization.
Presentation / Conference Contribution
CHRISTIE, L.A. 2020. Decentralized combinatorial optimization. In Bäck, T., Preuss, M., Deutz, A., Wang, H., Doerr, C., Emmerich, M. and Trautmann, H. (eds.) Parallel problem solving from nature: PPSN XVI: proceedings of the 16th Parallel problem solving from nature international conference (PPSN 2020), 5-9 September 2020, Leiden, Netherlands. Theoretical computer science and general issues, 12269. Cham; Springer, pages 360-372. Available from: https://doi.org/10.1007/978-3-030-58112-1_25

Combinatorial optimization is a widely-studied class of computational problems with many theoretical and real-world applications. Optimization problems are typically tackled using hardware and software controlled by the user. Optimization can be comp... Read More about Decentralized combinatorial optimization..

Partial structure learning by subset Walsh transform.
Presentation / Conference Contribution
CHRISTIE, L.A., LONIE, D.P. and MCCALL, J.A.W. 2013. Partial structure learning by subset Walsh transform. In Jin, Y. and Thomas, S.A. (eds.) Proceedings of the 13th UK workshop on computational intelligence (UKCI 2013), 9-11 September 2013, Guildford, UK. New York: IEEE [online], article number 6651297, pages 128-135. Available from: https://doi.org/10.1109/UKCI.2013.6651297

Estimation of distribution algorithms (EDAs) use structure learning to build a statistical model of good solutions discovered so far, in an effort to discover better solutions. The non-zero coefficients of the Walsh transform produce a hypergraph rep... Read More about Partial structure learning by subset Walsh transform..

Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings.
Presentation / Conference Contribution
CHRISTIE, L.A., MCCALL, J.A.W. and LONIE, D.P. 2014. Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings. In Igel, C. (ed.) Proceedings of the 2014 Genetic and evolutionary computation conference (GECCO 2014): a recombination of the 23rd International conference on genetic algorithms (ICGA-2014), and the 19th Annual genetic programming conference (GP-2014), 12-16 July 2014, Vancouver, Canada. New York: ACM [online], pages 333-340. Available from: https://doi.org/10.1145/2576768.2598240

Problem structure, or linkage, refers to the interaction between variables in a black-box fitness function. Discovering structure is a feature of a range of algorithms, including estimation of distribution algorithms (EDAs) and perturbation methods (... Read More about Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings..

Generating easy and hard problems using the proximate optimality principle.
Presentation / Conference Contribution
MCCALL, J.A.W., CHRISTIE, L.A. and BROWNLEE, A.E.I. 2015. Generating easy and hard problems using the proximate optimality principle. In Silva, S. (ed.) Proceedings of the companion publication of the 2015 annual conference on genetic and evolutionary computation (GECCO Companion '15), 11-15 July 2015, Madrid, Spain. New York: ACM [online], pages 767-768. Available from: https://doi.org/10.1145/2739482.2764890

We present an approach to generating problems of variable difficulty based on the well-known Proximate Optimality Principle (POP), often paraphrased as similar solutions have similar fitness. We explore definitions of this concept in terms of metrics... Read More about Generating easy and hard problems using the proximate optimality principle..

Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems.
Presentation / Conference Contribution
BROWNLEE, A.E.I., MCCALL, J.A.W. and CHRISTIE, L.A. 2015. Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems. In Proceedings of the 2015 IEEE congress on evolutionary computation (CEC 2015), 25-28 May 2015, Sendai, Japan. Piscataway, NJ: IEEE [online], pages 2066-2073. Available from: https://doi.org/10.1109/CEC.2015.7257139

Metaheuristics assume some kind of coherence between decision and objective spaces. Estimation of Distribution algorithms approach this by constructing an explicit probabilistic model of high fitness solutions, the structure of which is intended to r... Read More about Structural coherence of problem and algorithm: an analysis for EDAs on all 2-bit and 3-bit problems..

Investigating benchmark correlations when comparing algorithms with parameter tuning.
Presentation / Conference Contribution
CHRISTIE, L.A., BROWNLEE, A.E.I. and WOODWARD, J.R. 2018. Investigating benchmark correlations when comparing algorithms with parameter tuning. In Aguirre, H.E. (ed.) Proceedings of the 2018 Genetic and evolutionary computation conference companion (GECCO'18 companion), 15-19 July 2018, Kyoto, Japan. New York: Association for Computing Machinery [online], pages 209-210. Available from: https://doi.org/10.1145/3205651.3205747

Benchmarks are important for comparing performance of optimisation algorithms, but we can select instances that present our algorithm favourably, and dismiss those on which our algorithm under-performs. Also related are automated design of algorithms... Read More about Investigating benchmark correlations when comparing algorithms with parameter tuning..