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Dr Sandy Brownlee

Bibliometrics

Google Scholar (17/3/25)Scopus (17/3/25)
Citations23421273
h-index2821

Google Scholar author page

Scopus author page

DBLP index

ResearchGate profile

ORCID ID 0000-0003-2892-5059

Wherever possible, in addition to the publisher record, I've provided links to the preprint or open access versions of articles below. If you are having trouble accessing any of my papers, please contact me by clicking my name at the foot of this page.

Publications - peer reviewed journals

  1. Brownlee, A. E. I. and Vanmosuinck, E. R. O. (2025). Surrogate-assisted evolutionary multi-objective optimisation of office building glazing. Industrial Artificial Intelligence. Accepted, to appear
  2. Kashyap, G. S., Malik, K., Wazir, S., and Brownlee, A. E. I. (2025). Optimization of the rectangle area inside a concave polygon using PSO and Tabu Search. Soft Computing. Accepted, to appear DOI:
  3. Brownlee, A.E.I., Callan, J., Even-Mendoza, K., Geiger, A., Hanna, C., Petke, J., Sarro, F., and Sobania, D. (2024). Large Language Model Based Mutations in Genetic Improvement. Automated Software Engineering. DOI:10.1007/s10515-024-00473-6
  4. Zhou, R.; Bacardit, J.; Brownlee, A.E.I.; Cagnoni, S.; Fyvie, M.; Iacca, G.; McCall, J.A.W.; van Stein, N.; Walker, D.; Hu, T. (2024). Evolutionary Computation and Explainable AI: A Roadmap to Transparent Intelligent Systems. IEEE Transactions on Evolutionary Computation, vol 29, issue 5, pp 2213-2228. DOI:10.1109/TEVC.2024.3476443
  5. Watkinson, M., Brownlee, A.E.I. (2024). Comparing Apples and Oranges? Investigating the Consistency of CPU and Memory Profiler Results Across Multiple Java Versions. Automated Software Engineering, vol 31, article 28. DOI:10.1007/s10515-024-00423-2
  6. 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. Accepted, to appear DOI: 10.1111/exsy.13494
  7. Petke, J., Alexander, B., Barr, E.T., Brownlee, A. E. I., Wagner, M. & White, D.R. (2023) Program Transformation Landscapes for Automated Program Modification Using Gin. Empirical Software Engineering, vol 28, article 104. DOI: 10.1007/s10664-023-10344-5
  8. Wang, X., Brownlee, A. E. I., Weiszer, M., Woodward, J. R. W., Mahfouf, M., and Chen, J. (2022). An Interval Type-2 Fuzzy Logic Based Map Matching Algorithm for Airport Ground Movements. IEEE Transactions on Fuzzy Systems, vol 31, issue 2, pp. 582-595. DOI:10.1109/TFUZZ.2022.3221793
  9. Brownlee, A. E. I., Epitropakis, M. G., Mulder, J., Paelinck, M., and Burke, E. K. (2022) A systematic approach to parameter optimization and its application to flight schedule simulation software. Journal of Heuristics, vol 28, pages 509-538. DOI:10.1007/s10732-022-09501-8 (open access at publisher)
  10. Wang, X., Brownlee, A. E. I., Weiszer, M., Woodward, J. R. W., Mahfouf, M., and Chen, J. (2021). A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties. Transportation Research Part C: Emerging Technologies, vol. 132, Nov 2021, 103382. DOI:10.1016/j.trc.2021.103382 (open access at publisher)
  11. Swan, J., Adriaensen, S., Brownlee, A.E.I., Hammond, K., Johnson, C. G., Kheiri, A., Krawiec, F, Merelo, J. J., Minku, L. L., Özcan, E., Pappa, G. L., García-Sánchez, P., Sörensen, K., Voß, S., Wagner, M., White, D. R. (2021). Metaheuristics "In the Large". European Journal of Operational Research, Volume 297, Issue 2, 1 March 2022, Pages 393-406. DOI:10.1016/j.ejor.2021.05.042 (open access at publisher)
  12. Wang, X., Brownlee, A. E. I., Woodward, J. R. W., Weiszer, M., Mahfouf, M., and Chen, J. (2020). Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies, vol 124, 102892. DOI:10.1016/j.trc.2020.102892 - Preprint available here
  13. Brownlee, A. E. I., Wright, J. A., He, M., Lee, T, and McMenemy, P. (2020). A novel encoding for separable large-scale multi-objective problems and its application to the optimisation of housing stock improvements. Applied Soft Computing, vol 96, 106650. DOI:10.1016/j.asoc.2020.106650 - Preprint available here
  14. Wang, Z., Brownlee, A. E. I., and Wu, Q. (2020). Production And Joint Emission Reduction Decisions Based On Two-way Cost-sharing Contract Under Cap-and-trade Regulation. Computers and Industrial Engineering, vol 146, 106549, DOI:10.1016/j.cie.2020.106549
  15. Ochoa, G., Christie, L. A., Brownlee, A. E. I. and Hoyle, A. (2020). Multi-objective Evolutionary Design of Antibiotic Treatments. Artificial Intelligence in Medicine, vol 102, DOI:10.1016/j.artmed.2019.101759
  16. Brownlee, A. E. I., Swan, J., Senington, R. and Kocsis, Z. A. (2019). Conflict-free routing of multi-stop warehouse trucks. Optimization Letters, vol 14 issue 6, pp 1459-1470. DOI:10.1007/s11590-019-01453-6 (open access at publisher)
  17. Brownlee, A. E. I., Weiszer, M., Chen, J., Ravizza, S., Woodward, J.R., and Burke, E.K. (2018). A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation. Transportation Research Part C: Emerging Technologies, vol 92, July, pp 150-175. DOI:10.1016/j.trc.2018.04.020 (open access at publisher)
  18. Brownlee, A. E. I., Burles, N. and Swan, J. (2017). Search-based energy optimization of some ubiquitous algorithms. IEEE Transactions on Emerging Topics in Computational Intelligence, vol 1, issue 3, pp. 188-201. DOI:10.1109/TETCI.2017.2699193 (open access at publisher)
  19. Benlic, U., Brownlee, A. E. I. and Burke, E.K. (2016). Heuristic search for the coupled runway sequencing and taxiway routing problem. Transportation Research Part C: Emerging Technologies, vol 71, pp.333-355. DOI:10.1016/j.trc.2016.08.004 (open access at publisher)
  20. Wang, M., Wright, J. A., Brownlee, A. E. I. and Buswell, R.A. (2016). A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis. Energy and Buildings, vol 127, pp.313-326. DOI:10.1016/j.enbuild.2016.05.065 (preprint available via Stirling repository)
  21. Brownlee, A. E. I. and Wright, J. A. (2015). Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation. Applied Soft Computing, vol.33, pp 114-126. DOI:10.1016/j.asoc.2015.04.010 (open access at publisher)
  22. McKinstray, R., Lim, J. B., Tanyimboh, T. T., Phan, D. T., Sha, W., Brownlee, A. E (2014). Topographical optimisation of single-storey non-domestic steel framed buildings using photovoltaic panels for net-zero carbon impact. Building and Environment, vol. 86, pp.120-131. DOI:10.1016/j.buildenv.2014.12.017 (preprint available via Stirling repository)
  23. Wright, J.A.W., Brownlee, A.E.I., Mourshed, M.M., Wang, M. (2014). Multi-objective Optimization of Cellular Fenestration by an Evolutionary Algorithm, Journal of Building Performance Simulation, vol.7, issue 1, pp.33-51. DOI:10.1080/19401493.2012.762808 (preprint available via Loughborough repository)
  24. Brownlee, A.E.I., McCall, J.A.W., Zhang, Q (2013). Fitness Modelling with Markov Networks, IEEE Transactions on Evolutionary Computation, vol.17, issue 6, pp. 862-879. DOI:10.1109/TEVC.2013.2281538
  25. Brownlee, A. E. I., Regnier-Coudert, O., McCall, J. A. W., Massie, S. & Stulajter, S. (2013). An application of a GA with Markov network surrogate to feature selection, International Journal of Systems Science, vol. 44, issue 11, pp.2039-2056. DOI:10.1080/00207721.2012.684449

Publications - peer reviewed book chapters

  1. Kashyap, G. S., Sohlot, J., Siddiqui, A., Siddiqui, R., Malik, K., Wazir, S., and Brownlee, A. E. I. (2023). Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19, in: Research Advances in Network Technologies (Volume 2). CRC Press.
  2. Kashyap, G. S., Siddiqui, A., Siddiqui, R., Malik, K., Wazir, S., and Brownlee, A. E. I. (2023). Prediction of Suicidal Risk using Machine Learning Models, in: Research Advances in Intelligent Computing (Volume 2). CRC Press.
  3. Wazir, S., Kashyap, G. S., Malik, K., and Brownlee, A. E. I. (2023). Predicting the Infection Level of Covid-19 Virus using Normal Distribution Based Approximation Model and PSO, in: Hammouch, Z., Lahby, M., and Baleanu, D., Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer. DOI:10.1007/978-3-031-33183-1_5
  4. Shakya, S. K., McCall, J. A. W., Brownlee, A. E. I. & Owusu, G. (2012). DEUM - Distribution Estimation Using Markov Networks, in: Shakya, S. K. and Santana, R., Markov Networks in Evolutionary Computation. pp. 55-71. Springer. (invited book chapter) DOI:10.1007/978-3-642-28900-2_4
  5. Brownlee, A. E. I., McCall, J. A. W. & Shakya, S. K. (2012). The Markov Network Fitness Model, in: Shakya, S. K. and Santana, R., Markov Networks in Evolutionary Computation. pp. 125-140. Springer. (invited book chapter) DOI:10.1007/978-3-642-28900-2_8
  6. McCall, J. A. W., Brownlee, A. E. I. & Shakya, S. K. (2012). Applications of Distribution Estimation Using Markov Network Modelling (DEUM), in: Shakya, S. K. and Santana, R., Markov Networks in Evolutionary Computation. pp. 193-207. Springer. (invited book chapter) DOI:10.1007/978-3-642-28900-2_12
  7. Brownlee, A. E. I., McCall, J. A. W., Shakya, S. K. & Zhang, Q. (2009). Structure Learning and Optimisation in a Markov-network based Estimation of Distribution Algorithm. (extended version of earlier conference paper) Invited book chapter in "Chen, Y (ed.) Exploitation of Linkage Learning in Evolutionary Algorithms", Springer 2010. ISBN 978-3-642-12834-9
  8. Shakya, S. K., Brownlee, A. E. I., McCall, J. A. W., Fournier, F. & Owusu, G. (2009). A fully multivariate DEUM algorithm. (extended version of earlier conference paper) Invited book chapter in "Chen, Y (ed.) Exploitation of Linkage Learning in Evolutionary Algorithms", Springer 2010. ISBN 978-3-642-12834-9

Publications - peer reviewed conferences

  1. Even-Mendoza, K., Brownlee, A.E.I., Geiger, A., Hanna, C., Petke, J., Sarro, F., and Sobania, D. LLM-Guided Genetic Improvement: Envisioning Semantic Aware Automated Software Evolution. Proc. IEEE/ACM International Conference on Automated Software engineering 2025, Seoul, South Korea. Accepted, to appear
  2. Catalano, G.A.P.I., Brownlee, A.E.I., Cairns, D.E., Ainslie, R., and McCall, J.A.W. Interpretable Decision Trees to Predict Solution Fitness. Proc. of the Genetic and Evolutionary Computation Conference (GECCO) 2025, Malaga, Spain. Accepted, to appear
  3. Catalano, G.A.P.I., Brownlee, A.E.I., Cairns, D.E., McCall, J.A.W., and Ainslie, R. Evaluating a Novel Explainability Method for Metaheuristics via a User Study. Companion Proc. of the Genetic and Evolutionary Computation Conference (GECCO) 2025, Malaga, Spain. Accepted, to appear
  4. Catalano, G.A.P.I., Brownlee, A.E.I., Cairns, D.E., McCall, J.A.W., Ainslie, R., and Fyvie, M. Explaining a Staff Rostering Problem using Partial Solutions. Proceedings of the SGAI International Conference on Artificial Intelligence 2024. Cambridge, England. Accepted, to appear
  5. Brownlee, A.E.I., Thomson, S.L., and Oladapo, R. A Bi-Level Approach to Vehicle Fleet Reduction: Successful Case Study in Community Healthcare. Industrial Applications of Metaheuristics Workshop in: Companion Proc. of the Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia. Accepted, to appear
  6. Adair, J., Thomson, S.L., and Brownlee, A.E.I. Explaining evolutionary feature selection via local optima networks. Evolutionary Computation and Explainable AI Workshop in: Companion Proc. of the Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia. Accepted, to appear
  7. Catalano, G.A.P.I., Brownlee, A.E.I., Cairns, D.E., McCall, J.A.W., and Ainslie, R. Mining Potentially Explanatory Patterns via Partial Solutions. Companion Proc. of the Genetic and Evolutionary Computation Conference (GECCO) 2024, Melbourne, Australia. Accepted, to appear
  8. Brownlee, A.E.I., Callan, J., Even-Mendoza, K., Geiger, A., Hanna, C., Petke, J., Sarro, F., and Sobania, D. Enhancing Genetic Improvement Mutations Using Large Language Models. Proc. SSBSE Challenge Track 2023, San Francisco CA, United States. Accepted, to appear ArXiv
  9. Sobania, D., Geiger, A., Callan, J., Brownlee, A.E.I., Hanna, C., Moussa, R., Zamorano, M., Petke, J., and Sarro, F. Evaluating Explanations for Software Patches Generated by Large Language Models. Proc. SSBSE Challenge Track 2023, San Francisco CA, United States. Accepted, to appear PDF
  10. Fyvie, M., McCall, J.A.W., Christie, L.A., Zavoianu, A-C., Brownlee, A.E.I., and Ainslie, R. Explaining A Staff Rostering Problem By Mining Trajectory Variance Structures. Proceedings of the SGAI International Conference on Artificial Intelligence 2023. Cambridge, England. Accepted, to appear
  11. Fyvie, M., McCall, J.A.W., Christie, L.A., and Brownlee, A.E.I. Explaining a Staff Rostering Genetic Algorithm using Sensitivity Analysis and Trajectory Analysis. Evolutionary Computation and Explainable AI Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2023, Lisbon, Portugal. Accepted, to appear
  12. Thomson, S.L., Adair, J., Brownlee, A.E.I., and van den Berg, D. From Fitness Landscapes to Explainable AI and Back. Evolutionary Computation and Explainable AI Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2023, Lisbon, Portugal. Accepted, to appear
  13. Watkinson, M., Brownlee, A.E.I. Updating Gin's profiler for current Java. Proceedings of the Genetic Improvement Workshop, International Conference on Software Engineering 2023. Accepted, to appear
  14. Bacardit, J., Brownlee, A.E.I., Cagnoni, S., Iacca, G., McCall, J.A.W., Walker, D. The intersection of Evolutionary Computation and Explainable AI. Evolutionary Computation and Explainable AI Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2022, Boston MA, USA. Accepted, to appear
  15. Singh, M., Brownlee,A.E.I., and Cairns, D.E. Towards Explainable Metaheuristics: Mining Surrogate Fitness Models for Importance of Variables. Evolutionary Computation and Explainable AI Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2022, Boston MA, USA. Accepted, to appear
  16. Wallace, A., Brownlee, A.E.I., and Cairns, D.E. Towards explaining metaheuristic solution quality by data mining surrogate fitness models for importance of variables. Proceedings of the SGAI International Conference on Artificial Intelligence 2021. Cambridge, England / Virtual. Artificial Intelligence XXXVIII. SGAI-AI 2021. Lecture Notes in Computer Science, vol 13101. Springer, Cham. DOI:10.1007/978-3-030-91100-3_5 Preprint here
  17. Brownlee, A.E.I., Adair, J., Haraldsson, S.O., and Jabbo, J. Exploring the Accuracy - Energy Trade-off in Machine Learning. Proceedings of the Genetic Improvement Workshop, International Conference on Software Engineering 2021. Madrid, Spain / Virtual. Video and preprint here
  18. Brownlee, A.E.I., Petke, J., and Rasburn, A. Injecting Shortcuts for Faster Running Java Code. Proc. WCCI 2020, Glasgow, Scotland / Virtual. 10.1109/CEC48606.2020.9185708 - Paper - Slides - Video
  19. Petke, J. and Brownlee, A.E.I. Software Improvement with Gin: a Case Study. Proc. SSBSE Challenge Track 2019, Tallinn, Estonia, pp 183-189. DOI:10.1007/978-3-030-27455-9_14
  20. Alexander, B., Barr, E.T., Brownlee, A. E. I., Petke, J., Wagner, M. & White, D.R. A Survey of Genetic Improvement Search Spaces. Genetic Improvement Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2019, Prague, Czech Republic, pp 1715-1721. DOI:10.1145/3319619.3326870
  21. Brownlee, A. E. I., Petke, J., Alexander, B., Barr, E.T., Wagner, M. & White, D.R. Gin: Genetic Improvement Research Made Easy. Proc. of the Genetic and Evolutionary Computation COnference 2019, Prague, Czech Republic, pp 985-993. DOI:10.1145/3321707.3321841
  22. Reid, K. N., Li, J., Brownlee, A. E. I., Kern, M., Veerapen, N., Swan, J. & Owusu, G. A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem. Proc. of the Genetic and Evolutionary Computation COnference 2019, Prague, Czech Republic, pp 1311-1318. DOI:10.1145/3321707.3321769
  23. Brownlee, A. E. I., Kim, S-J., Wan, S-H., Chan, S. & Lawson, J. A. Crowd-Sourcing the Sounds of Places with a Web-Based Evolutionary Algorithm. Companion Proc. of the Genetic and Evolutionary Computation COnference 2019, Prague, Czech Republic, pp 131-132. DOI:10.1145/3319619.3322028 (technical report with detailed experiments and results via Stirling repository)
  24. Brownlee, A. E. I., Woodward, J.R., Weiszer, M. & Chen, J. A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing. Proc. of the Genetic and Evolutionary Computation COnference 2018, Kyoto, Japan, pp 1207-1213. DOI:10.1145/3205455.3205558 (preprint available via Stirling repository)
  25. Brownlee, A. E. I., Woodward, J.R. & Veerapen, N. Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features. Poster in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2018, Kyoto, Japan, pp 135-136. DOI:10.1145/3205651.3205748 (preprint available via Stirling repository) - further details and analysis in technical report
  26. Christie, L.A., Brownlee, A. E. I. & Woodward, J.R. Investigating Benchmark Correlations when Comparing Algorithms with Parameter Tuning. Poster in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2018, Kyoto, Japan, pp 209-210. DOI:10.1145/3205651.3205747 (preprint available via Stirling repository) - further details and analysis in technical report
  27. Adair, J., Brownlee, A.E.I & Ochoa, G. Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces. Proc. EvoApplications 2018 (LNCS 10784) pp 63-77. DOI: 10.1007/978-3-319-77538-8_5
  28. Adair, J., Brownlee, A.E.I, Daolio, F., & Ochoa, G. Evolving Training Sets for Improved Transfer Learning in Brain Computer Interfaces. MOD 2017 - The Third International Conference on Machine Learning, Optimization and Big Data (LNCS 10710), pp 186-197. DOI: 10.1007/978-3-319-72926-8_16
  29. Haraldsson, S.O., Woodward, J.R., Brownlee, A. E. I., Smith, A.V. & Gudnason, V. F. Genetic improvement of runtime and its fitness landscape in a bioinformatics application. Genetic Improvement Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2017, Berlin, Germany, pp 1521-1528. DOI: 10.1145/3067695.3082526
  30. Haraldsson, S.O., Woodward, J.R., Brownlee, A. E. I. & Siggeirsdottir, K. Fixing Bugs in Your Sleep: How Genetic Improvement Became an Overnight Success. Genetic Improvement Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2017, Berlin, Germany, pp 1513-1520. DOI: 10.1145/3067695.3082517
  31. Haraldsson, S.O., Woodward, J.R. and Brownlee, A.E.I. The Use of Automatic Test Data Generation for Genetic Improvement in a Live System. Search-Based Software Testing Workshop in: IEEE/ACM International Conference on Software Engineering 2017, Buenos Ares, Argentina, pp 28-31. DOI: 10.1109/SBST.2017.10
  32. Haraldsson, S.O., Woodward, J.R., Brownlee, A.E.I. and Cairns, D.E. Exploring Fitness and Edit Distance of Mutated Python Programs. In: Proc. EuroGP 2017, Amsterdam, Netherlands (LNCS 10196), pp 19-34. DOI: 10.1007/978-3-319-55696-3_2
  33. Adair, J., Brownlee, A. and Ochoa, G. Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces. In: Proc. UK Workshop on Computational Intelligence 2016. DOI:10.1007/978-3-319-46562-3_19 (preprint available via Stirling repository)
  34. Brownlee, A. E. I. Mining Markov Network Surrogates for Value Added Optimisation. Surrogate Assisted Evolutionary Optimisation (SAEOpt) Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2016, Denver, CO, USA. DOI:10.1145/2908961.2931711 - slides (preprint available via Stirling repository)
  35. Woodward, J.R., Johnson, C.G, & Brownlee, A. E. I. Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming. Evolutionary Computation for the Automated Design of Algorithms (ECADA) Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2016, Denver, CO, USA. DOI:10.1145/2908961.2931728 (preprint available here)
  36. Woodward, J.R., Brownlee, A. E. I. & Johnson, C.G. Evals is not enough: why we should report wall-clock time. Genetic Improvement Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2016, Denver, CO, USA. DOI:10.1145/2908961.2931695 (preprint available via Stirling repository)
  37. Woodward, J.R., Johnson, C.G, & Brownlee, A. E. I. GP vs GI: if you can't beat them, join them. Genetic Improvement Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2016, Denver, CO, USA. DOI:10.1145/2908961.2931694 (preprint available via Stirling repository)
  38. He, M., Brownlee, A. E. I., Wright, J. A., Taylor, S. C. Multi-dwelling Refurbishment Optimization: Problem Decomposition, Solution, and Trade-off Analysis. Proc. IBPSA Building Simulation Conference 2015, Hyderabad, India. pp.2066-2072. Paper
  39. Burles, N., Bowles, E., Brownlee, A.E.I., Kocsis, Z., Swan, J. and Veerapen, N. Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava. Proc. SSBSE Challenge Track 2015, Bergamo, Italy. LNCS 9275. pp. 255-261. Springer. DOI:10.1007/978-3-319-22183-0_20 (preprint available via Stirling repository)
  40. Kocsis, Z., Brownlee, A., Swan, J., Senington, R. Haiku - a Scala combinator toolkit for semi-automated composition of metaheuristics. Proc. SSBSE 2015, Bergamo, Italy. LNCS 9275. pp. 125-140. Springer. DOI:10.1007/978-3-319-22183-0_9 (preprint available via Stirling repository)
  41. He, M., Lee, T., Taylor, S. C., Brownlee, A. E. I., Wright, J. A. Multi-objective optimization for a large scale retrofit program for the housing stock in the North East of England. 6th International Building Physics Conference, IBPC 2015, Turin, Italy. Energy Procedia vol. 78, November 2015, pp. 854-859. DOI: 10.1016/j.egypro.2015.11.007 (preprint available via Stirling repository)
  42. Brownlee, A. E. I., Woodward, J.R. and Swan, J. Metaheuristic Design Pattern: Surrogate Fitness Functions. MetaDeeP Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2015, Madrid, Spain. pp.1261-1264. ACM Press. DOI:10.1145/2739482.2768499 Slides (preprint available via Stirling repository)
  43. Swan, J., Bowles, E., Burles, N., Brownlee, A. E. I., Kocsis, Z.A. and Veerapen, N. Embedded Dynamic Improvement Programming. Genetic Improvement Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2015, Madrid, Spain. pp.831-832. ACM Press.
  44. Brownlee, A. E. I., McCall, J. A. W., and Christie, Lee A. Structural Coherence of Problem and Algorithm: An Analysis for EDAs on all 2-bit and 3-bit Problems. Proc. of the Congress on Evolutionary Computation 2015, Sendai, Japan. pp.2066-2073. IEEE Press. (Nominated for best paper)
  45. Kocsis, Z., Neumann, G., Swan, J., Epitropakis, M., Brownlee, A., Haraldsson, S. and Bowles, E. Repairing and optimizing Hadoop hashCode implementations. Proc. SSBSE Challenge Track, Fortaleza, Brazil. pp.259-264. Springer, 2014. (full text on ResearchGate)
  46. Brownlee, A. E. I., Swan, J., Özcan, E. and Parkes, A. J. Hyperion2 - A toolkit for {meta-, hyper-} heuristic research. EvoSoft Workshop in: Companion Proc. of the Genetic and Evolutionary Computation COnference 2014, Vancouver, BC, Canada. pp.1133-1140. ACM Press.
  47. Wang, M., Wright, J., Brownlee, A., and Buswell, R. A Comparison of Approaches to Stepwise Regression Analysis for Variables Sensitivity Measurements Used with a Multi-Objective Optimization Problem. Proc. ASHRAE 2014 Annual Conf., Seattle, WA, 2014.
  48. Wang, M., Wright, J., Brownlee, A., and Buswell, R. Applying Global and Local SA in Identification of Variables Importance with the use of Multi-Objective Optimization. Proc. 2nd IBPSA-England Building Simulation and Optimization Conf., 2014.
  49. Watson, V., Jones, E., Murphy, E., Wright, J., Brownlee, A. and Aird, G. (2013). Industry challenges in using optimisation tools with IES Optimise as a case study. CIBSE Technical Symposium, Liverpool John Moores University, Liverpool, UK.
  50. Wang, M., Wright, J., Brownlee, A., and Buswell, R. A comparison of approaches to stepwise regression for global sensitivity analysis used with evolutionary optimization. Pro. 13th Int. IBPSA Conf., 2013, 2551-2558.
  51. Brownlee, A. E. I. and Wright, J. A. (2012). Solution Analysis in Multi-Objective Optimization. Building Simulation and Optimization Conference, Loughborough, UK, pp.317-324. IBPSA England.
  52. Wright, J. A. W., Wang, M., Brownlee, A. E. I. & Buswell, R. A. (2012). Variable Convergence in Evolutionary Optimization and its Relationship to Sensitivity Analysis, Building Simulation and Optimization Conference, Loughborough, UK, pp. 102-109. IBPSA England.
  53. Brownlee, A. E. I., McCall, J. A. W., Pelikan, M. (2012). Influence of selection on structure learning in markov network EDAs: an empirical study., Proc. of the Genetic and Evolutionary Computation COnference 2012, Philadelphia, PA, USA, pp. 249-256. ACM Press.
  54. Brownlee, A. E. I., Wright, J. A. & Mourshed, M. M. (2011). A multi-objective window optimisation problem, Proc. of the Genetic and Evolutionary Computation COnference 2011, Dublin, ROI, pp. 89-90. ACM Press.
  55. Brownlee, A. E. I., Regnier-Coudert, O., McCall, J. A. W. & Massie, S. (2010). Using a Markov Network as a Surrogate Fitness Function in a Genetic Algorithm, Proc. of the Congress on Evolutionary Computation 2010, Barcelona, Spain, pp. 4525-4532. IEEE Press.
  56. Brownlee, A. E. I., McCall, J. A. W., Shakya, S. K. & Zhang, Q. (2009). Structure Learning and Optimisation in a Markov-network based Estimation of Distribution Algorithm, Proc. of the Congress on Evolutionary Computation 2009, Trondheim, Norway, pp. 447-454. IEEE Press.
  57. Shakya, S. K., Brownlee, A. E. I., McCall, J. A. W., Fournier, F. & Owusu, G. (2009). A fully multivariate DEUM algorithm, Proc. of the Congress on Evolutionary Computation 2009, Trondheim, Norway, pp. 479-486. IEEE Press.
  58. Brownlee, A. E. I., Wu, Y., McCall, J. A. W., Godley, P. M., Cairns, D. E. & Cowie, J. (2008). Optimisation and fitness modelling of bio-control in mushroom farming using a Markov network EDA, Proc. of the Genetic and Evolutionary Computation COnference 2008, Atlanta, Georgia, USA, pp. 465-466. ACM Press.
  59. Brownlee, A. E. I., Pelikan, M., McCall, J. A.W. & Petrovski, A. (2008). An application of a multivariate estimation of distribution algorithm to cancer chemotherapy, Proc. of the Genetic and Evolutionary Computation COnference 2008, Atlanta, Georgia, USA, pp. 463-464. ACM Press. Tech report with full results and discussion on arXiv:2205.08438
  60. Brownlee, A. E. I., McCall, J. A. W., Zhang, Q. & Brown, D. (2008). Approaches to Selection and their Effect on Fitness Modelling in an Estimation of Distribution Algorithm, Proc. of the World Congress on Computational Intelligence 2008, Hong Kong, China, pp. 2621-2628. IEEE Press.
  61. Wu, Y., McCall, J., Godley, P., Brownlee, A. & Cairns, D. (2008). Bio-control in mushroom farming using a Markov network EDA, Proc. of the World Congress on Computational Intelligence 2008, Hong Kong, China, pp. 2996-3001. IEEE Press.
  62. Petrovski, A., Brownlee, A. and McCall, J. (2005). Statistical optimisation and tuning of GA factors, Proc. of the Congress on Evolutionary Computation 2005, Edinburgh, UK, pp. 758-764. IEEE Press.

Publications - other

  1. Bacardit, J., Brownlee, A.E.I., Cagnoni, S., Iacca, G., McCall, J.A.W., Walker, D. (2024). Introduction to the Special Issue on Explainable AI in Evolutionary Computation, ACM Transactions on Evolutionary Learning and Optimization, Volume 4, Issue 1. DOI:10.1145/3649144
  2. Brownlee, A.E.I. (2023). Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality, Proc. of the Genetic and Evolutionary Computation COnference 2023, Lisbon, Portugal, Late Breaking Papers. ACM Press.
  3. Bacardit, J., Brownlee, A.E.I., Cagnoni, S., Iacca, G., McCall, J.A.W., Walker, D. (2023) Evolutionary Computation and Explainable AI: a year in review. Late-breaking Abstracts, EvoStar Conference 2023, Brno, Czech Republic. Accepted, to appear
  4. Brownlee, A. E. I. (2021) Genetic Improvement @ ICSE 2021: Personal reflection of a Workshop Participant. ACM SIGSOFT Software Engineering Notes, Volume 46, Issue 4, October 2021. pp 28-30. DOI:10.1145/3485952.3485960
  5. Brownlee, A. E. I., Wallace, A., Cairns, D.E. (2021) Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation. SICSA Workshop on eXplainable Artificial Intelligence (XAI). Available on CEUR
  6. Brownlee, A. E. I., Atkin, J. A., Woodward, J. R., Burke, E. K. (2020) Methods And Sources For Underpinning Airport Ground Movement Decision Support Systems. Technical Report. Available here
  7. Brownlee, A. E. I., Woodward, J.R. & Veerapen, N. Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features Poster (Detailed Experiments and Results) Technical Report to accompany GECCO paper. Available here
  8. Christie, L.A., Brownlee, A. E. I. & Woodward, J.R. Investigating Benchmark Correlations when Comparing Algorithms with Parameter Tuning. (Detailed Experiments and Results) Technical Report to accompany GECCO paper. Available here
  9. Haraldsson, S.O., Brownlee, A.E.I. and Woodward, J.R. (2017). Computers will soon be able to fix themselves - are IT departments for the chop?, The Conversation.
  10. Brownlee, A.E.I. and Swan, J. (2017). Never mind the iPhone X, battery life could soon take a great leap forward, The Conversation.
  11. Brownlee, A.E.I. and Woodward, J.R. Why we fell out of love with algorithms inspired by nature, The Conversation.
  12. McCall, J.A.W., Christie, L.A. and Brownlee, A.E.I. (2015). Generating Easy and Hard Problems using the Proximate Optimality Principle, Proc. of the Genetic and Evolutionary Computation COnference 2015, Madrid, Spain, Late Breaking Papers. pp.767-768 ACM Press.
  13. Brownlee, A.E.I., Benlic, U. and Burke, E.K. (2014). Air traffic control about to let pilots plan their own routes - but don't worry, The Conversation.
  14. Brownlee, A.E.I., Atkin, J.A.D., Woodward, J.R., Benlic, U. and Burke, E.K. (2014). Airport Ground Movement: Real World Data Sets and Approaches to Handling Uncertainty, Proc. of the Practice and Theory of Automated Timetabling (PATAT) Conference, York, UK, pp. 462-464. Extended abstract for presentation. Abstract | Slides
  15. Brownlee, A.E.I. (2009). Multivariate Markov networks for fitness modelling in an estimation of distribution algorithm. PhD Thesis, Robert Gordon University, UK.
  16. Brownlee, A.E.I., McCall, J.A.W. and Brown, D.F. (2007). Solving the MAXSAT Problem using a Multivariate EDA based on Markov Networks, Proc. of the Genetic and Evolutionary Computation COnference 2007, London, UK, Late Breaking Papers. ACM Press.
  17. Brownlee, A.E.I. (2005). An Application of Genetic Algorithms to University Timetabling, undergraduate honours project report, Robert Gordon University, UK. Related presentation slides here.

Seminars, Presentations and External Invited Talks

University of Dundee Seminar "Automatically Improving Software Using Genetic Improvement and Large Language Models" - 12 September 2024

Genetic Improvement: Improving Real-World Source Code with Search - tutorial at GECCO 2024 - Saemundur O. Haraldsson, Alexander Brownlee, Markus Wagner, and John Woodward - slides

SICSA Post-EC Day 2023 Invited Keynote "Why's it done that? Explainable AI, EAs, and Exploration of Design" - 1 September 2023

Genetic Improvement: Improving Real-World Source Code with Search - tutorial at GECCO 2023 - Saemundur O. Haraldsson, Alexander Brownlee, and John Woodward - slides

BCS Real AI 2022 "Sustainable Building Design through Evolutionary Algorithms and Optimisation" - slides - 30 September 2022

Joint MSU/UoS seminar "Modelling and Optimisation of Aircraft Ground Movement for Greener, Faster Airport Operations" watch here - 16 March 2022

Genetic Improvement: Improving Real-World Source Code with Search - tutorial at GECCO 2022 - Saemundur O. Haraldsson, Alexander Brownlee, and John Woodward

EasyJet Data Science Seminar "Modelling and Optimisation of Aircraft Ground Movement for Greener, Faster Airport Operations" - 16 March 2022

UHI Data Science Seminar "Modelling and Optimisation of Aircraft Ground Movement for Greener, Faster Airport Operations" - 3 March 2022

Genetic Improvement: Improving Real-World Source Code with Search - tutorial at GECCO 2021 - Saemundur O. Haraldsson, Alexander Brownlee, Markus Wagner, Bradley Alexander, and John Woodward

Improving trust in the results of search-based optimisation - Seminar in the AI for Aviation Sustainability series - online - 29 June 2021 - video here

Improving trust in the results of search-based optimisation - Seminar at BT / Tommy Flowers Network - online - 21 January 2021 - video here

Genetic Improvement: Improving Real-World Source Code with Search - tutorial at PPSN 2020 - Alexander Brownlee, Saemundur O. Haraldsson, and John Woodward

Search-based approaches to improving the energy consumption of Java programs - invited talk at the 62nd Crest Open Workshop (on Automatic Program Repair and Genetic Improvement), UCL, London, 21 January 2020 - slides - talk (480p) (720p)

Computer Says No! Explaining the Decisions of Machines - Public lecture, Univ. of Stirling, 25 April 2019 - slides.

An Overview of Artificial Intelligence - talk given to Stirling Probus Club, 25 October 2017

Planes, training and optimobiles: adding value to optimisation in the real world - Univ. of Stirling, 29 September 2017 - slides

Funding Hints and Tips from the EPSRC - COSMOS talk - slides

A whistle-stop tour of LaTeX - COSMOS talk, joint with Nada Veerapen - slides pt 1 and slides pt 2

Hyper-parameter tuning to improve existing software - invited talk at the 50th Crest Open Workshop (on Genetic Improvement), UCL, London, 31 January 2017 - slides - talk (480p) (720p)

Adding value to optimisation by interrogating fitness models - presentation at Model Based Evolutionary Algorithms workshop, GECCO, 20 July 2016 - slides

Weighing up the options: Finding the right solution when lots of things matter - Public lecture, Univ. of Stirling, 17 March 2016 - slides.

Airport ground movement: Real world data sets and approaches to handling uncertainty - LANCS workshop on Air Transportation at Univ. of Nottingham - slides available on linked page - 21/2/2014

Fitness modelling for better optimisation and decision making - Univ. of Stirling, 29 Sept 2013 - slides

Multi-objective Optimisation of Building Designs - Robert Gordon University, 9 Dec 2011 - slides

Markov Networks & Fitness Modelling in Evolutionary Algorithms - Univ. of Stirling, 17 Oct 2008

Author-izer links to articles published by ACM

ACM DL Author-ize serviceMining Markov Network Surrogates for Value-Added Optimisation
Alexander E.I. Brownlee
GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016
ACM DL Author-ize serviceEvals is Not Enough: Why We Should Report Wall-clock Time
John R. Woodward, Alexander E.I. Brownlee, Colin G. Johnson
GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016
ACM DL Author-ize serviceGP vs GI: If You Can't Beat Them, Join Them
John R. Woodward, Colin G. Johnson, Alexander E.I. Brownlee
GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016
ACM DL Author-ize serviceConnecting Automatic Parameter Tuning, Genetic Programming as a Hyper-heuristic, and Genetic Improvement Programming
John R. Woodward, Colin G. Johnson, Alexander E.I. Brownlee
GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, 2016
ACM DL Author-ize serviceGenerating Easy and Hard Problems using the Proximate Optimality Principle
John A.W. McCall, Lee A. Christie, Alexander E.I. Brownlee
GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
ACM DL Author-ize serviceEmbedded Dynamic Improvement
Nathan Burles, Jerry Swan, Edward Bowles, Alexander E.I. Brownlee, Zoltan A. Kocsis, Nadarajen Veerapen
GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
ACM DL Author-ize serviceMetaheuristic Design Pattern: Surrogate Fitness Functions
Alexander E.I. Brownlee, John R. Woodward, Jerry Swan
GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation, 2015
ACM DL Author-ize serviceHyperion2: a toolkit for {meta-, hyper-} heuristic research
Alexander E.I. Brownlee, Jerry Swan, Ender Özcan, Andrew J. Parkes
GECCO Comp '14 Proceedings of the Companion Publication of the 2014 Annual Conference on Genetic and Evolutionary Computation, 2014
ACM DL Author-ize serviceInfluence of selection on structure learning in markov network EDAs: an empirical study
Alexander E.I. Brownlee, John A.W. McCall, Martin Pelikan
GECCO '12 Proceedings of the 14th annual conference on Genetic and evolutionary computation, 2012
ACM DL Author-ize serviceA multi-objective window optimisation problem
Alexander E.I. Brownlee, Jonathan A. Wright, Monjur M. Mourshed
GECCO '11 Proceedings of the 13th annual conference companion on Genetic and evolutionary computation, 2011
ACM DL Author-ize serviceSolving the MAXSAT problem using a multivariate EDA based on Markov networks
Alexander E. I. Brownlee, John A. W. McCall, Deryck F. Brown
GECCO '07 Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, 2007

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