Skip to main content

Research Repository

Advanced Search

Towards explainable metaheuristics: PCA for trajectory mining in evolutionary algorithms. (2021)
Conference Proceeding
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..

Optimising the introduction of connected and autonomous vehicles in a public transport system using macro-level mobility simulations and evolutionary algorithms. (2021)
Presentation / Conference
HAN, K., CHRISTIE, L.A., ZĂVOIANU, 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..

Non-deterministic solvers and explainable AI through trajectory mining. (2021)
Conference Proceeding
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. (2020)
Conference Proceeding
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..

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. (2018)
Conference Proceeding
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..

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)
Dataset
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)
Dataset
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].

Generating easy and hard problems using the proximate optimality principle. (2015)
Conference Proceeding
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. (2015)
Conference Proceeding
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..

Minimal walsh structure and ordinal linkage of monotonicity-invariant function classes on bit strings. (2014)
Conference Proceeding
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..

Partial structure learning by subset Walsh transform. (2013)
Conference Proceeding
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..