Senior Lecturer in Computing Science
Computing Science and Mathematics - Division Room 4B125 University of Stirling, Stirling, FK9 4LA
Contact: if you have an enquiry about the Big Data or AI MSc Programmes, please email big-data@stir.ac.uk or artificial-intelligence@stir.ac.uk. For any other communications, I can be reached at alexander.brownlee@stir.ac.uk or on +44 (0)1786 467454.
I'm a Senior Lecturer within Computing Science and Mathematics at Stirling, leader of the Data Science and Intelligent Systems research group and also a member of Computational Mathematics and Optimisation research group. I am co-lead of the AI Theme for SICSA, helping to connect and support the AI research community in Scotland. I am also a Visiting Fellow in Civil and Building Engineering at Loughborough University. I'm interested in explainable or value-added optimisation: techniques that yield optimal or near-optimal solutions but also reveal underlying information about the problem to help people make informed decisions. My main focus is in metaheuristics, including evolutionary algorithms and estimation of distribution algorithms; related issues such as fitness modelling (and mining such models), handling constraints and multiple objectives, and decision support. I am also interested in the underlying theory of what makes particular algorithms suited to particular problems. I have applied this work to application areas including scheduling and simulation-based optimisation in civil engineering and transport, software engineering, healthcare, and art.
More detail on my interests and activities can be found on my CSM webpages, under "My personal webpage" above.
I'm broadly interested in optimisation and machine learning, including real world applications of these and underpinning theory.
More details can be found here: https://www.cs.stir.ac.uk/~sbr/research.html
Background to my work
I have always been fascinated by computing and in particular artificial intelligence techniques. I particularly enjoy the interplay between the theoretical side of understanding what makes different algorithms tick and the huge range of interesting application areas that have meaningful real-world value (or are just fun!). My work has settled around approaches to dealing with real-world optimisation problems; handling uncertainty, solving problems with hard constraints and multiple objectives, dealing with long simulation run-times and analysis of optimisation results to better help with decision making. I completed a Computer Science BSc(hons) in 2005 at Robert Gordon University in Aberdeen, Scotland. During the last year of that degree I was funded by the Carnegie Trust to conduct a short-term research project in applying genetic algorithms to cancer chemotherapy scheduling. My interest in this area grew, leading to an honours project in timetabling with memetic algorithms. I then progressed to work for a PhD, entitled Multivariate Markov Networks for Fitness Modelling in an Estimation of Distribution Algorithm. This covered a range of applications for evolutionary algorithms, and focussed on the construction of fitness models to support the evolutionary process. I continued to research alongside a job as a software engineer in industry during 2008-2010, where I was working in the sector of oil, gas and renewable energy. I returned to full-time as a research associate in the building energy group at Loughborough University, and subsequently came to the CHORDS research group (now part of the Data Science group) here at Stirling in 2013.
AirOpt - optimisation of airspace to save 2MT CO2 a year
PI: Dr Sandy Brownlee
Funded by: Innovate UK
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Innovation Voucher: Huli Route Matching
PI: Dr Sandy Brownlee
Funded by: Scottish Funding Council
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Innovation Voucher: Mindstream connect
PI: Dr Sandy Brownlee
Funded by: Scottish Funding Council
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Know Your Numbers Business and Personal Wealth Planner
PI: Dr Sandy Brownlee
Funded by: Scottish Funding Council
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Airspace Optimisation Tool
PI: Dr Sandy Brownlee
Funded by: Scottish Funding Council
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Towards grammar-aware operators for automatic improvement of software
PI: Dr Sandy Brownlee
Funded by: The Carnegie Trust
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TRANSIT: Towards a Robust Airport Decision Support System for Intelligent Taxiing
PI: Dr Sandy Brownlee
Funded by: Engineering and Physical Sciences Research Council
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Crowd-sourcing the aural identities of places by evolutionary optimisation
PI: Dr Sandy Brownlee
Funded by: Scottish Crucible
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Conference Proceeding
Graham K, Thomson S & Brownlee A (2023) Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality. In: GECCO '23 Companion: Proceedings of the Companion Conference on Genetic and Evolutionary Computation. The Genetic and Evolutionary Computation Conference (GECCO) 2023, Lisbon, 15.07.2023-19.07.2023. New York: ACM, pp. 67-68.
Conference Proceeding
Explaining A Staff Rostering Problem By Mining Trajectory Variance Structures
Fyvie M, McCall JAW, Christie LA, Zavoianu A, Brownlee AEI & Ainslie R (2023) Explaining A Staff Rostering Problem By Mining Trajectory Variance Structures. In: TBC. Lecture Notes in Artificial Intelligence. AI-2023 Forty-third SGAI International Conference on Artificial Intelligence, Cambridge, 12.12.2023-14.12.2023. Cham, Switzerland: Springer.
Conference Proceeding
Evaluating Explanations for Software Patches Generated by Large Language Models
Sobania D, Geiger A, Callan J, Brownlee A, Hanna C, Moussa R, Zamorano López M, Petke J & Sarro F (2023) Evaluating Explanations for Software Patches Generated by Large Language Models. In: Symposium on Search-Based Software Engineering- Challenge Track, San Francisco, CA, USA, 08.12.2023-08.12.2023.
Book Chapter
Enhancing Genetic Improvement Mutations Using Large Language Models
Brownlee A, Callan J, Even-Mendoza K, Geiger A, Hanna C, Petke J, Sarro F & Sobania D (2023) Enhancing Genetic Improvement Mutations Using Large Language Models. In: Arcaini P, Yue T & Fredericks EM (eds.) Search-Based Software Engineering: 15th International Symposium, SSBSE 2023, San Francisco, CA, USA, December 8, 2023, Proceedings. Lecture Notes in Computer Science. Cham, Switzerland: Springer. https://link.springer.com/book/9783031487958
Article
Towards Explainable Metaheuristics: Feature Extraction from Trajectory Mining
Fyvie M, Mccall J, Christie L, Brownlee A & Singh M (2023) Towards Explainable Metaheuristics: Feature Extraction from Trajectory Mining. Expert Systems, Art. No.: e13494. https://doi.org/10.1111/exsy.13494
Conference Proceeding
From Fitness Landscapes to Explainable AI and Back
Thomson S, Adair J, Brownlee A & van den Berg D (2023) From Fitness Landscapes to Explainable AI and Back. In: GECCO '23 Companion. Gecco '23: The Genetic and Evolutionary Computation Conference, Lisbon, 15.07.2023-19.07.2023. New York: ACM. https://doi.org/10.1145/3583133.3596395
Book Chapter
Prediction of Suicidal Risk using Machine Learning Models
Kashyap G, Siddiqui A, Siddiqui R, Malik K, Wazir S & Brownlee A Prediction of Suicidal Risk using Machine Learning Models. In: Research Advances in Intelligent Computing (Volume 2). CRC Press / Yalor and Francis.
Book Chapter
Detection of a facemask in real-time using deep learning methods: Prevention of Covid 19
Kashyap G, Sohlot J, Siddiqui A, Siddiqui R, Malik K, Wazir S & Brownlee A (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 / Taylor and Francis.
Article
Program Transformation Landscapes for Automated Program Modification Using Gin
Petke J, Alexander B, Barr ET, Brownlee A, Wagner M & White D (2023) Program Transformation Landscapes for Automated Program Modification Using Gin. Empirical Software Engineering.
Conference Proceeding
Updating Gin's profiler for current Java
Watkinson M & Brownlee A (2023) Updating Gin's profiler for current Java. Wagner M (Researcher) In: GI '23: Proceedings of the 12th International Workshop on Genetic Improvement. The 12th International Workshop on Genetic Improvement, at the International Conference on Software Engineering, Melbourne, Australia, 14.05.2023-20.05.2023. New York: ACM.
Book Chapter
Wazir S, Kashyap GS, Malik K & Brownlee A (2023) Predicting the Infection Level of Covid-19 Virus using Normal Distribution Based Approximation Model and PSO. In: Mathematical Modeling and Intelligent Control for Combating Pandemics. Springer.
Other
Evolutionary Computation and Explainable AI: a year in review
Bacardit J, Brownlee A, Cagnoni S, Iacca G, McCall J & Walker D (2023) Evolutionary Computation and Explainable AI: a year in review. EvoStar, Brno, Czech Republic (hybrid), 12.04.2023-14.04.2023.
Article
An Interval Type-2 Fuzzy Logic Based Map Matching Algorithm for Airport Ground Movements
Wang X, Brownlee AEI, Weiszer M, Woodward JR, Mahfouf M & Chen J (2022) An Interval Type-2 Fuzzy Logic Based Map Matching Algorithm for Airport Ground Movements. IEEE Transactions on Fuzzy Systems, 31 (2), pp. 582-595. https://doi.org/10.1109/TFUZZ.2022.3221793
Conference Proceeding
Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables
Singh M, Brownlee AEI & Cairns D (2022) Towards Explainable Metaheuristic: Mining Surrogate Fitness Models for Importance of Variables. In: GECCO '22: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO '22:, Boston, USA, 09.07.2022-13.07.2022. New York: ACM, pp. 1785-1793. https://doi.org/10.1145/3520304.3533966
Conference Proceeding
The intersection of Evolutionary Computation and Explainable AI Anonymous authors
Bacardit J, Brownlee A, Cagnoni S, Iacca G, McCall J & Walker D (2022) The intersection of Evolutionary Computation and Explainable AI Anonymous authors. In: Genetic and Evolutionary Computation Conference: GECCO '22, Boston, MA, USA, 09.07.2022-13.07.2022. New York: ACM. https://gecco-2022.sigevo.org/HomePage
Article
Brownlee AEI, Epitropakis MG, Mulder J, Paelinck M & Burke EK (2022) A systematic approach to parameter optimization and its application to flight schedule simulation software. Journal of Heuristics. https://doi.org/10.1007/s10732-022-09501-8
Article
Swan J, Adriaensen S, Brownlee AEI, Hammond K, Johnson CG, Kheiri A, Krawiec F, Merelo JJ, Minku LL, Ozcan E, Pappa GL, García-Sánchez P, Sorensen K, Voß S, Wagner M & White DR (2022) Metaheuristics "In the Large". European Journal of Operational Research, 297 (2), pp. 393-406. https://doi.org/10.1016/j.ejor.2021.05.042
Conference Proceeding
Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation
Brownlee A, Wallace A & Cairns D (2021) Mining Markov Network Surrogates to Explain the Results of Metaheuristic Optimisation. In: Martin K, Wiratunga N & Wijekoon A (eds.) Proceedings of the SICSA eXplainable Artifical Intelligence Workshop 2021. CEUR Workshop Proceedings, 2894. SICSA eXplainable Artifical Intelligence Workshop 2021, Aberdeen, 01.06.2021-01.06.2021. Aachen: CEUR Workshop Proceedings, pp. 64-70. http://ceur-ws.org/Vol-2894/short9.pdf
Conference Proceeding
Exploring the Accuracy - Energy Trade-off in Machine Learning
Brownlee A, Adair J, Haraldsson S & Jabbo J (2021) Exploring the Accuracy - Energy Trade-off in Machine Learning. In: 2021 IEEE/ACM International Workshop on Genetic Improvement (GI). Genetic Improvement Workshop at 43rd International Conference on Software Engineering, Madrid, Spain, 30.05.2021-30.05.2021. Piscataway, NJ: IEEE. https://doi.org/10.1109/GI52543.2021.00011
Conference Proceeding
Wallace A, Brownlee AEI & Cairns D (2021) Towards explaining metaheuristic solution quality by data mining surrogate fitness models for importance of variables. In: Bramer M & Ellis R (eds.) Artificial Intelligence XXXVIII. Lecture Notes in Computer Science, 13101. 41st SGAI International Conference on Artificial Intelligence, AI 2021, Cambridge, 14.12.2021-16.12.2021. Cham, Switzerland: Springer, pp. 58-72. https://doi.org/10.1007/978-3-030-91100-3_5
Article
Wang X, Brownlee AEI, Weiszer M, Woodward JR, Mahfouf M & Chen J (2021) A chance-constrained programming model for airport ground movement optimisation with taxi time uncertainties. Transportation Research Part C: Emerging Technologies, 132, Art. No.: 103382. https://doi.org/10.1016/j.trc.2021.103382
Commentary
Genetic Improvement @ ICSE 2021: Personal reflection of a Workshop Participant
Brownlee A (2021) Genetic Improvement @ ICSE 2021: Personal reflection of a Workshop Participant. ACM SIGSOFT Software Engineering Notes, 46 (4), pp. 28-30. https://doi.org/10.1145/3485952.3485960
Article
Aircraft taxi time prediction: Feature importance and their implications
Wang X, Brownlee AEI, Woodward JR, Weiszer M, Mahfouf M & Chen J (2021) Aircraft taxi time prediction: Feature importance and their implications. Transportation Research Part C: Emerging Technologies, 124, Art. No.: 102892. https://doi.org/10.1016/j.trc.2020.102892
Conference Proceeding
Injecting Shortcuts for Faster Running Java Code
Brownlee AEI, Petke J & Rasburn AF (2020) Injecting Shortcuts for Faster Running Java Code. In: 2020 IEEE Congress on Evolutionary Computation (CEC). IEEE World Congress on Computational Intelligence, Glasgow, 19.07.2020-24.07.2020. Piscataway, NJ, USA: IEEE, pp. 1-8. https://wcci2020.org/; https://doi.org/10.1109/CEC48606.2020.9185708
Article
Brownlee A, Wright J, He M, Lee T & 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, 96, Art. No.: 106650. https://doi.org/10.1016/j.asoc.2020.106650
Article
Conflict-free routing of multi-stop warehouse trucks
Brownlee A, Swan J, Senington R & Kocsis Z (2020) Conflict-free routing of multi-stop warehouse trucks. Optimization Letters, 14 (6), p. 1459–1470. https://doi.org/10.1007/s11590-019-01453-6
Article
Wang Z, Brownlee A & 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, 146, Art. No.: 106549. https://doi.org/10.1016/j.cie.2020.106549
Technical Report
Methods And Sources For Underpinning Airport Ground Movement Decision Support Systems
Brownlee A, Atkin J, Woodward J & Burke E Methods And Sources For Underpinning Airport Ground Movement Decision Support Systems. None.
Article
Multi-objective Evolutionary Design of Antibiotic Treatments
Ochoa G, Christie LA, Brownlee AE & Hoyle A (2020) Multi-objective Evolutionary Design of Antibiotic Treatments. Artificial Intelligence in Medicine, 102, Art. No.: 101759. https://doi.org/10.1016/j.artmed.2019.101759
Conference Proceeding
A Survey of Genetic Improvement Search Spaces
Petke J, Alexander B, Barr ET, Brownlee AEI, Wagner M & White DR (2019) A Survey of Genetic Improvement Search Spaces. In: López-Ibáñez M (ed.) GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO '19 - Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: Association for Computing Machinery, pp. 1715-1721. https://doi.org/10.1145/3319619.3326870
Conference Proceeding
Software Improvement with Gin: A Case Study
Petke J & Brownlee AEI (2019) Software Improvement with Gin: A Case Study. In: Nejati S & Gay G (eds.) Search-Based Software Engineering. SSBSE 2019. Lecture Notes in Computer Science, 11664. 11th International Symposium on Search Based Software Engineering, Tallinn, Estonia, 31.08.2019-01.09.2019. Cham, Switzerland: Springer Verlag, pp. 183-189. https://doi.org/10.1007/978-3-030-27455-9_14
Conference Proceeding
A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem
Reid KN, Li J, Brownlee A, Kern M, Veerapen N, Swan J & Owusu G (2019) A Hybrid Metaheuristic Approach to a Real World Employee Scheduling Problem. In: Proceedings of the Genetic and Evolutionary Computation Conference 2019. GECCO '19: The Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM, pp. 1311-1318. https://doi.org/10.1145/3321707.3321769
Technical Report
Crowd Sourcing The Sounds Of Places With A Web Based Genetic Algorithm Techreport
Brownlee A, Kim S, Wang S, Chan S & Lawson JA (2019) Crowd Sourcing The Sounds Of Places With A Web Based Genetic Algorithm Techreport. University of Stirling. Stirling: University of Stirling.
Conference Proceeding
Crowd-Sourcing the Sounds of Places with a Web-Based Evolutionary Algorithm
Brownlee AEI, Kim S, Wang S, Chan S & Lawson JA (2019) Crowd-Sourcing the Sounds of Places with a Web-Based Evolutionary Algorithm. In: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference Companion. GECCO 2019: The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM, pp. 131-132. https://doi.org/10.1145/3319619.3322028
Conference Proceeding
Gin: Genetic Improvement Research Made Easy
Brownlee AEI, Petke J, Alexander B, Barr ET, Wagner M & White DR (2019) Gin: Genetic Improvement Research Made Easy. In: GECCO '19: Proceedings of the Genetic and Evolutionary Computation Conference. GECCO 2019: The Genetic and Evolutionary Computation Conference, Prague, Czech Republic, 13.07.2019-17.07.2019. New York: ACM, pp. 985-993. https://doi.org/10.1145/3321707.3321841
Conference Proceeding
Adair J, Brownlee A & Ochoa G (2018) Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces. In: Applications of Evolutionary Computation. EvoApplications 2018. Lecture Notes in Computer Science, 10784. EvoStar 2018, Parma, Italy, 04.04.2018-06.04.2018. Cham, Switzerland: Springer, pp. 63-77. https://link.springer.com/chapter/10.1007/978-3-319-77538-8_5; https://doi.org/10.1007/978-3-319-77538-8_5
Conference Proceeding
A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing
Brownlee A, Woodward JR, Weiszer M & Chen J (2018) A Rolling Window with Genetic Algorithm Approach to Sorting Aircraft for Automated Taxi Routing. In: Proceedings of the Genetic and Evolutionary Computation Conference 2018. GECCO 2018: The 2018 conference on Genetic and Evolutionary Computation, Kyoto, Japan, 15.07.2018-19.07.2018. New York: ACM, pp. 1207-1213. http://gecco-2018.sigevo.org/index.html/tiki-index.php?page=HomePage; https://doi.org/10.1145/3205455.3205558
Conference Proceeding
Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features
Brownlee A, Woodward JR & Veerapen N (2018) Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features. In: Proceedings of GECCO 2018. Genetic and Evolutionary Computation Conference 2018, 15.07.2018-19.07.2018. New York: ACM, pp. 135-136. https://doi.org/10.1145/3205651.3205748
Conference Proceeding
Investigating Benchmark Correlations when Comparing Algorithms with Parameter Tuning
Christie LA, Brownlee A & Woodward JR (2018) Investigating Benchmark Correlations when Comparing Algorithms with Parameter Tuning. In: Proceedings of the Genetic and Evolutionary Computation Conference Companion. Genetic and Evolutionary Computation Conference 2018, 15.07.2018-19.07.2018. New York: ACM, pp. 209-210. https://doi.org/10.1145/3205651.3205747
Article
A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation
Brownlee A, Weiszer M, Chen J, Ravizza S, Woodward JR & Burke EK (2018) A Fuzzy Approach to Addressing Uncertainty in Airport Ground Movement Optimisation. Transportation Research Part C: Emerging Technologies, 92, pp. 150-175. https://doi.org/10.1016/j.trc.2018.04.020
Technical Report
Christie LA, Brownlee A & Woodward JR (2018) Investigating Benchmark Correlations when Comparing Algorithms with Parameter Tuning (Detailed Experiments and Results). Not applicable. Stirling: University of Stirling.
Technical Report
Brownlee A, Woodward JR & Veerapen N (2018) Relating Training Instances to Automatic Design of Algorithms for Bin Packing via Features (Detailed Experiments and Results). Not applicable. Stirling: University of Stirling.
Conference Proceeding
Fixing bugs in your sleep: How genetic improvement became an overnight success
Haraldsson S, Woodward J, Brownlee A & Siggeirsdottir K (2017) Fixing bugs in your sleep: How genetic improvement became an overnight success. In: 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017. GECCO 2017: The Genetic and Evolutionary Computation Conference, Berlin, Germany, 15.07.2017-19.07.2017. New York: Association for Computing Machinery, Inc, pp. 1513-1520. https://doi.org/10.1145/3067695.3082517
Conference Proceeding
Genetic Improvement of Runtime and its Fitness Landscape in a Bioinformatics Application
Haraldsson S, Woodward J, Brownlee A, Smith AV & Gudnason V (2017) Genetic Improvement of Runtime and its Fitness Landscape in a Bioinformatics Application. In: 2017 Genetic and Evolutionary Computation Conference Companion, GECCO 2017. GECCO 2017: The Genetic and Evolutionary Computation Conference, Berlin, Germany, 15.07.2017-19.07.2017. New York: Association for Computing Machinery, Inc, pp. 1521-1528. https://doi.org/10.1145/3067695.3082526
Newspaper Article
Computers will soon be able to fix themselves – are IT departments for the chop?
Haraldsson S, Brownlee A & Woodward JR (2017) Computers will soon be able to fix themselves – are IT departments for the chop?. The Conversation. 12.10.2017. https://theconversation.com/computers-will-soon-be-able-to-fix-themselves-are-it-departments-for-the-chop-85632
Newspaper Article
Never mind the iPhone X, battery life could soon take a great leap forward
Brownlee A & Swan J (2017) Never mind the iPhone X, battery life could soon take a great leap forward. The Conversation. 13.09.2017. https://theconversation.com/never-mind-the-iphone-x-battery-life-could-soon-take-a-great-leap-forward-83901
Article
Search-based energy optimization of some ubiquitous algorithms
Brownlee A, Burles N & Swan J (2017) Search-based energy optimization of some ubiquitous algorithms. IEEE Transactions on Emerging Topics in Computational Intelligence, 1 (3), pp. 188-201. https://doi.org/10.1109/TETCI.2017.2699193
Conference Proceeding
Exploring Fitness and Edit Distance of Mutated Python Programs
Haraldsson S, Woodward J, Brownlee A & Cairns D (2017) Exploring Fitness and Edit Distance of Mutated Python Programs. In: McDermott J, Castelli M, Sekanina L, Haasdijk E & García-Sánchez P (eds.) Genetic Programming: 20th European Conference, EuroGP 2017, Amsterdam, The Netherlands, April 19-21, 2017, Proceedings. Lecture Notes in Computer Science, 10196. EuroGP 2017: Genetic Programming, Amsterdam, The Netherlands, 19.04.2017-21.04.2017. Cham: Springer International Publishing, pp. 19-34. https://doi.org/10.1007/978-3-319-55696-3_2
Other
Hyper-parameter tuning to improve existing software
Brownlee A (2017) Hyper-parameter tuning to improve existing software. [Video] 31.01.2017. http://crest.cs.ucl.ac.uk/cow/50/videos/brownlee_cow50_720p.mp4
Conference Proceeding
Mining Markov Network Surrogates for Value-Added Optimisation
Brownlee A (2016) Mining Markov Network Surrogates for Value-Added Optimisation. In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. Genetic and Evolutionary Computation Conference GECCO’16, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1267-1274. https://doi.org/10.1145/2908961.2931711
Conference Proceeding
Woodward J, Johnson C & Brownlee A (2016) Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming. In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. GECCO 2016: Genetic and Evolutionary Computation Conference, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1357-1358. https://doi.org/10.1145/2908961.2931728
Conference Proceeding
Evals is not enough: why we should report wall-clock time
Woodward J, Brownlee A & Johnson C (2016) Evals is not enough: why we should report wall-clock time. In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. GECCO 2016: Genetic and Evolutionary Computation Conference, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1157-1158. https://doi.org/10.1145/2908961.2931695
Conference Proceeding
GP vs GI: if you can't beat them, join them
Woodward J, Johnson C & Brownlee A (2016) GP vs GI: if you can't beat them, join them. In: Friedrich T (ed.) GECCO '16 Companion Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion. Genetic and Evolutionary Computation Conference, GECCO-2016, Denver, CO, USA, 20.07.2016-24.07.2016. New York: ACM, pp. 1155-1156. https://doi.org/10.1145/2908961.2931694
Article
Heuristic search for the coupled runway sequencing and taxiway routing problem
Benlic U, Brownlee A & Burke EK (2016) Heuristic search for the coupled runway sequencing and taxiway routing problem. Transportation Research Part C: Emerging Technologies, 71, pp. 333-355. https://doi.org/10.1016/j.trc.2016.08.004
Article
Wang M, Wright J, Brownlee A & Buswell R (2016) A comparison of approaches to stepwise regression on variables sensitivities in building simulation and analysis. Energy and Buildings, 127, pp. 313-326. https://doi.org/10.1016/j.enbuild.2016.05.065
Conference Proceeding
Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces
Adair J, Brownlee A & Ochoa G (2016) Evolutionary Algorithms with Linkage Information for Feature Selection in Brain Computer Interfaces. In: Angelov P, Gegov A, Jayne C & Shen Q (eds.) Advances in Computational Intelligence Systems: Contributions Presented at the 16th UK Workshop on Computational Intelligence, September 7–9, 2016, Lancaster, UK. Advances in Intelligent Systems and Computing, 513. UKCI 2016 - 16th UK Workshop on Computational Intelligence, Lancaster, 07.09.2016-09.09.2016. London: Springer, pp. 287-307. https://doi.org/10.1007/978-3-319-46562-3_19
Conference Proceeding
Metaheuristic Design Pattern: Surrogate Fitness Functions
Brownlee A, Woodward J & Swan J (2016) Metaheuristic Design Pattern: Surrogate Fitness Functions. In: Silva S (ed.) GECCO Companion '15 Proceedings of the Companion Publication of the 2015 Annual Conference on Genetic and Evolutionary Computation. GECCO 2015: Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, 11.07.2015-15.07.2015. New York: ACM, pp. 1261-1264. https://doi.org/10.1145/2739482.2768499
Conference Proceeding
Generating Easy and Hard Problems using the Proximate Optimality Principle
McCall J, Christie LA & Brownlee A (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. 2015 Annual Conference on Genetic and Evolutionary Computation, Madrid, Spain, 11.07.2015-15.07.2015. New York: ACM, pp. 767-768. http://dl.acm.org/citation.cfm?id=2764890; https://doi.org/10.1145/2739482.2764890
Conference Proceeding
Multi-dwelling Refurbishment Optimization: Problem Decomposition, Solution, and Trade-off Analysis
He M, Brownlee A, Wright JA & Taylor S (2015) Multi-dwelling Refurbishment Optimization: Problem Decomposition, Solution, and Trade-off Analysis. In: Proceedings of BS2015: 14th Conference of International Building Performance Simulation Association, Hyderabad, India, Dec. 7-9, 2015. 4th International Conference of the International Building Performance Simulation Association (BS2015), Hyderabad, India, 07.12.2015-09.12.2015. International Building Performance Simulation Association (IBPSA), pp. 2066-2072. http://www.ibpsa.org/proceedings/BS2015/p2364.pdf
Conference Proceeding
Haiku - a Scala combinator toolkit for semi-automated composition of metaheuristics
Attila Kocsis Z, Brownlee A, Swan J & Senington R (2015) Haiku - a Scala combinator toolkit for semi-automated composition of metaheuristics. In: Barros M & Labiche Y (eds.) Search-Based Software Engineering: 7th International Symposium, SSBSE 2015, Bergamo, Italy, September 5-7, 2015, Proceedings. Lecture Notes in Computer Science, 9275. 7th International Symposium, SSBSE 2015, Bergamo, Italy, 05.09.2015-07.09.2015. Cham, Switzerland: Springer, pp. 125-140. https://doi.org/10.1007/978-3-319-22183-0_9
Conference Proceeding
Burles N, Swan J, Bowles E, Brownlee A, Attila Kocsis Z & Veerapen N (2015) Embedded Dynamic Improvement. In: Silva S (ed.) GECCO Companion '15 Proceedings of the Companion Publication of the 2015 on Genetic and Evolutionary Computation Conference. GECCO '15 Genetic and Evolutionary Computation Conference 2015, Madrid, Spain, 11.07.2015-15.07.2015. New York: ACM, pp. 831-832. https://doi.org/10.1145/2739482.2768423
Conference Paper
He M, Brownlee A, Lee T, Wright JA & Taylor S (2015) Multi-objective optimization for a large scale retrofit program for the housing stock in the North East of England. 6th International Building Physics Conference. Energy Procedia, 78, pp. 854-859. http://www.sciencedirect.com/science/article/pii/S1876610215017397; https://doi.org/10.1016/j.egypro.2015.11.007
Article
Brownlee AEI & Wright JA (2015) Constrained, mixed-integer and multi-objective optimisation of building designs by NSGA-II with fitness approximation. Applied Soft Computing, 33, pp. 114-126. https://doi.org/10.1016/j.asoc.2015.04.010
Conference Proceeding
Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava
Burles N, Bowles E, Brownlee A, Attila Kocsis Z, Swan J & Veerapen N (2015) Object-Oriented Genetic Improvement for Improved Energy Consumption in Google Guava. In: Barros M & Labiche Y (eds.) Search-Based Software Engineering. Lecture Notes in Computer Science, 9275. Symposium on Search-Based Software Engineering (SSBSE 2015), Bergamo, Italy, 05.09.2015-07.09.2015. Switzerland: Springer International Publishing, pp. 255-261. http://dx.doi.org/10.1007/978-3-319-22183-0_20; https://doi.org/10.1007/978-3-319-22183-0_20
Newspaper Article
Why we fell out of love with algorithms inspired by nature
Brownlee A & Woodward J (2015) Why we fell out of love with algorithms inspired by nature. The Conversation. 03.06.2015. https://theconversation.com/why-we-fell-out-of-love-with-algorithms-inspired-by-nature-42718
Conference Proceeding
Structural Coherence of Problem and Algorithm: An Analysis for EDAs on all 2-bit and 3-bit Problems
Brownlee A, McCall J & Christie LA (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. IEEE Congress on Evolutionary Computation 2015, Sendai, Japan, 25.05.2015-28.05.2015. Piscataway, NJ, USA: IEEE Press, pp. 2066-2073. https://doi.org/10.1109/CEC.2015.7257139
Article
McKinstray R, Lim J, Tanyimboh T, Phan D, Sha W & Brownlee A (2015) Topographical optimisation of single-storey non-domestic steel framed buildings using photovoltaic panels for net-zero carbon impact. Building and Environment, 86, pp. 120-131. https://doi.org/10.1016/j.buildenv.2014.12.017
Conference Proceeding
Repairing and Optimizing Hadoop hashCode Implementations
Attila Kocsis Z, Neumann G, Swan J, Epitropakis M, Brownlee A, Haraldsson S & Bowles E (2014) Repairing and Optimizing Hadoop hashCode Implementations. In: Le GC & Yoo S (eds.) Search-Based Software Engineering: 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, August 26-29, 2014. Proceedings. 6th International Symposium, SSBSE 2014, Fortaleza, Brazil, 26.08.2014-29.08.2014. Berlin Heidelberg: Springer, pp. 259-264. http://link.springer.com/chapter/10.1007/978-3-319-09940-8_21; https://doi.org/10.1007/978-3-319-09940-8_21
Conference Proceeding
Hyperion2: A Toolkit for {Meta-, Hyper-} Heuristic Research
Brownlee A, Swan J, Ozcan E & Parkes AJ (2014) Hyperion2: A Toolkit for {Meta-, Hyper-} Heuristic Research. In: Proceedings of the 2014 Conference Companion on Genetic and Evolutionary Computation Companion. GECCO Comp '14. GECCO 2014: Genetic and Evolutionary Computation Conference, Vancouver, BC, Canada, 12.07.2014-16.07.2014. New York, NY, USA: ACM, pp. 1133-1140. http://doi.acm.org/10.1145/2598394.2605687; https://doi.org/10.1145/2598394.2605687
Article
Multi-objective optimization of cellular fenestration by an evolutionary algorithm
Wright JA, Brownlee A, Mourshed MM & Wang M (2014) Multi-objective optimization of cellular fenestration by an evolutionary algorithm. Journal of Building Performance Simulation, 7 (1), pp. 33-51. https://doi.org/10.1080/19401493.2012.762808
Newspaper Article
Air traffic control about to let pilots plan their own routes – but don’t worry
Brownlee A, Burke E & Benlic U (2014) Air traffic control about to let pilots plan their own routes – but don’t worry. The Conversation. 16.12.2014. https://theconversation.com/air-traffic-control-about-to-let-pilots-plan-their-own-routes-but-dont-worry-35446
Lecture
Airport Ground Movement: Real World Data Sets and Approaches to Handling Uncertainty
Brownlee A, Atkin JAD, Woodward J, Benlic U & Burke E (2014) Airport Ground Movement: Real World Data Sets and Approaches to Handling Uncertainty (Presentation) PATAT 2014: 10th International Conference on the Practice and Theory of Automated Timetabling, York, 26.08.2014-29.08.2014. http://www.patatconference.org/patat2014/programme.pdf
Conference Proceeding
Wang M, Wright JA, Brownlee A & Buswell R (2014) A Comparison of Approaches to Stepwise Regression Analysis for Variables Sensitivity Measurements Used with a Multi-Objective Optimization Problem. In: ASHRAE Papers CD: 2014 ASHRAE Annual Conference, Seattle, WA. D-SE-14-C060. ASHRAE 2014 Annual Conference, Seattle, WA, USA, 28.06.2014-02.07.2014. Seattle, WA: ASHRAE. https://www.ashrae.org/membership--conferences/conferences/past-ashrae-conferences
Conference Proceeding
Wang M, Wright JA, Brownlee A & Buswell R (2014) Applying Global And Local SA In Identification Of Variables Importance With The Use Of Multi-Objective Optimization. In: Malki-Epsthein L, Spataru C, Halburd L & Mumovic D (eds.) Proceedings of the Building Simulation and Optimization Conference 2014. Building Simulation and Optimization 2014, London, UK, 23.06.2014-24.06.2014. London: The Bartlett, UCL Faculty of the Built Environment. http://www.bso14.org/BSO14_Papers/BSO14_Paper_096.pdf
Conference Proceeding
Wang M, Wright JA, Buswell R & Brownlee A (2013) A comparison of approaches to stepwise regression for global sensitivity analysis used with evolutionary optimization. In: Proceedings of BS2013: 13th Conference of International Building Performance Simulation Association, Chambéry, France, August 26-28. BS2013: 13th Conference of International Building Performance Simulation Association, Chambéry, France, 26.08.2013-28.08.2013. London: International Building Performance Simulation Association, pp. 2551-2558. http://www.ibpsa.org/proceedings/BS2013/p_1047.pdf
Article
Fitness modeling with markov networks
Brownlee A, McCall J & Zhang Q (2013) Fitness modeling with markov networks. IEEE Transactions on Evolutionary Computation, 17 (6), pp. 862-879. https://doi.org/10.1109/TEVC.2013.2281538
Article
An application of a GA with Markov network surrogate to feature selection
Brownlee A, Regnier-Coudert O, McCall J, Massie S & Stulajter S (2013) An application of a GA with Markov network surrogate to feature selection. International Journal of Systems Science, 44 (11), pp. 2039-2056. https://doi.org/10.1080/00207721.2012.684449
Conference Paper (unpublished)
Industry challenges in using optimisation tools with IES Optimise as a case study
Watson V, Jones E, Murphy E, Wright JA, Brownlee A & Aird G (2013) Industry challenges in using optimisation tools with IES Optimise as a case study. CIBSE Technical Symposium, Liverpool, UK, 11.04.2013-12.04.2013. http://www.cibse.org/knowledge/cibse-technical-symposium-2013/industry-challenges-in-using-optimisation-tools-wi
Conference Proceeding
Solution Analysis in Multi-Objective Optimization
Brownlee A & Wright JA (2012) Solution Analysis in Multi-Objective Optimization. In: Wright J & Cook M (eds.) Proceedings of the 2012 Building Simulation and Optimization Conference. First Building Simulation and Optimization Conference, Loughborough, 10.09.2012-11.09.2012. Loughborough: Loughborough University, pp. 317-324. http://www.bso12.org/-proceedings/papers/5A3.pdf
Conference Proceeding
Variable Convergence in Evolutionary Optimization and its Relationship to Sensitivity Analysis
Wright JA, Wang M, Brownlee A & Buswell R (2012) Variable Convergence in Evolutionary Optimization and its Relationship to Sensitivity Analysis. In: Wright J & Cook M (eds.) Proceedings of the 2012 Building Simulation and Optimization Conference. First Building Simulation and Optimization Conference, Loughborough, 10.09.2012-11.09.2012. Loughborough: Loughborough University, pp. 102-109. http://www.bso12.org/-proceedings/papers/2A2.pdf
Conference Proceeding
Influence of selection on structure learning in markov network EDAs: An empirical study
Brownlee A, McCall J & Pelikan M (2012) Influence of selection on structure learning in markov network EDAs: An empirical study. In: Soule T & Moore J (eds.) GECCO'12 - Proceedings of the 14th International Conference on Genetic and Evolutionary Computation. GECCO '12: 14th annual conference on Genetic and evolutionary computation, Philadelphia, USA, 07.07.2012-11.07.2012. New York, NY: ACM, pp. 249-256. http://dl.acm.org/citation.cfm?id=2330200
Book Chapter
The Markov network fitness model
Brownlee A, McCall J & Shakya SK (2012) The Markov network fitness model. In: Shakya S & Santana R (eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, 14. Berlin Heidelberg: Springer, pp. 125-140. http://link.springer.com/chapter/10.1007/978-3-642-28900-2_8#; https://doi.org/10.1007/978-3-642-28900-2_8
Book Chapter
DEUM - Distribution estimation using Markov networks
Shakya S, McCall J, Brownlee A & Owusu G (2012) DEUM - Distribution estimation using Markov networks. In: Shakya S & Santana R (eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, 14. Berlin Heidelberg: Springer, pp. 55-71. http://link.springer.com/chapter/10.1007/978-3-642-28900-2_4#
Book Chapter
Applications of distribution estimation using Markov Network Modelling (DEUM)
McCall J, Brownlee A & Shakya S (2012) Applications of distribution estimation using Markov Network Modelling (DEUM). In: Shakya S & Santana R (eds.) Markov Networks in Evolutionary Computation. Adaptation, Learning, and Optimization, 14. Berlin Heidelberg: Springer, pp. 193-207. http://link.springer.com/chapter/10.1007%2F978-3-642-28900-2_12
Conference Proceeding
A multi-objective window optimisation problem
Brownlee A, Wright JA & Mourshed MM (2011) A multi-objective window optimisation problem. In: Krasnogor N & Lanzi P (eds.) Genetic and Evolutionary Computation Conference, GECCO'11 - Companion Publication. 13th Annual Conference on Genetic and Evolutionary Computation, Dublin, Ireland, 12.07.2011-16.07.2011. New York, NY: ACM, pp. 89-90. http://dl.acm.org/citation.cfm?id=2001910
Book Chapter
DEUM – A Fully Multivariate EDA Based on Markov Networks
Shakya S, Brownlee A, McCall J, Fournier FA & Owusu G (2010) DEUM – A Fully Multivariate EDA Based on Markov Networks. In: Chen Y (ed.) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, 3. Berlin Heidelberg: Springer, pp. 71-93. http://link.springer.com/chapter/10.1007/978-3-642-12834-9_4
Conference Proceeding
Using a Markov network as a surrogate fitness function in a genetic algorithm
Brownlee A, Regnier-Coudert O, McCall J & Massie S (2010) Using a Markov network as a surrogate fitness function in a genetic algorithm. In: 2010 IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 IEEE Congress on Evolutionary Computation, CEC 2010. 2010 IEEE Congress on Evolutionary Computation (CEC), Barcelon, 18.07.2010-23.07.2010. Piscataway, NJ: IEEE. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=5586548&abstractAccess=no&userType=inst; https://doi.org/10.1109/CEC.2010.5586548
Book Chapter
Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm
Brownlee A, McCall J, Shakya SK & Zhang Q (2009) Structure Learning and Optimisation in a Markov Network Based Estimation of Distribution Algorithm. In: Chen Y (ed.) Exploitation of Linkage Learning in Evolutionary Algorithms. Evolutionary Learning and Optimization, 3. Berlin Heidelberg: Springer, pp. 45-69. http://link.springer.com/chapter/10.1007/978-3-642-12834-9_3#; https://doi.org/10.1007/978-3-642-12834-9_3
Conference Proceeding
Structure learning and optimisation in a markov-network based estimation of distribution algorithm
Brownlee A, McCall J, Shakya S & Zhang Q (2009) Structure learning and optimisation in a markov-network based estimation of distribution algorithm. In: IEEE Congress on Evolutionary Computation, 2009. CEC '09. Congress on Evolutionary Computation 2009, Trondheim, Norway, 18.05.2009-21.05.2009. Piscataway, NJ: IEEE, pp. 447-454. http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=4982980&refinements%3D4281221607%26sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A4982922%29; https://doi.org/10.1109/CEC.2009.4982980
Conference Proceeding
A fully multivariate DEUM algorithm
Shakya SK, Brownlee A, McCall J, Fournier FA & Owusu G (2009) A fully multivariate DEUM algorithm. In: IEEE Congress on Evolutionary Computation, 2009. CEC '09. IEEE Congress on Evolutionary Computation, 2009. CEC '09, Trondheim, 18.05.2009-21.05.2009. Piscataway, NJ: IEEE, pp. 479-486. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4982984&abstractAccess=no&userType=inst; https://doi.org/10.1109/CEC.2009.4982984
Conference Proceeding
An application of a multivariate estimation of distribution algorithm to cancer chemotherapy
Brownlee A, Pelikan M, McCall J & Petrovski A (2008) An application of a multivariate estimation of distribution algorithm to cancer chemotherapy. In: Keijzer M (ed.) GECCO '08 Proceedings of the 10th annual conference on Genetic and evolutionary computation. GECCO '08: 10th annual conference on Genetic and evolutionary computation, Atlanta, GA, USA, 12.07.2008-16.07.2008. New York, NY: ACM, pp. 463-464. http://dl.acm.org/citation.cfm?id=1389179; https://doi.org/10.1145/1389095.1389179
Conference Proceeding
Optimisation and Fitness Modelling of Bio-control in Mushroom Farming Using a Markov Network EDA
Brownlee A, Wu Y, McCall J, Godley PM, Cairns D & Cowie J (2008) Optimisation and Fitness Modelling of Bio-control in Mushroom Farming Using a Markov Network EDA. In: Keijzer M (ed.) Proceedings of the 10th annual conference on Genetic and evolutionary computation, (GECCO-2008). Genetic and Evolutionary Computation Conference, GECCO-2008, Atlanta, Georgia, 12.07.2008-16.07.2008. New York: Association for Computing Machinery (ACM), pp. 465-466. https://doi.org/10.1145/1389095.1389180
Conference Proceeding
Bio-control in Mushroom Farming Using a Markov Network EDA
Wu Y, McCall J, Godley PM, Brownlee A & Cairns D (2008) Bio-control in Mushroom Farming Using a Markov Network EDA. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on Evolutionary Computation 2008, CEC 2008, (IEEE World Congress on Computational Intelligence), Hong Kong, 01.06.2008-06.06.2008. Hoboken, NJ: Institute of Electrical and Electronics Engineers (IEEE), pp. 2991-2996. https://doi.org/10.1109/CEC.2008.4631201
Conference Proceeding
Brownlee A, McCall J, Zhang Q & Brown DF (2008) Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm. In: IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence). IEEE Congress on Evolutionary Computation, 2008. CEC 2008. (IEEE World Congress on Computational Intelligence), Hong Kong, 01.06.2008-06.06.2008. IEEE, pp. 2621-2628. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4631150&abstractAccess=no&userType=inst; https://doi.org/10.1109/CEC.2008.4631150
Conference Proceeding
Solving the MAXSAT problem using a multivariate EDA based on Markov networks
Brownlee A, McCall J & Brown DF (2007) Solving the MAXSAT problem using a multivariate EDA based on Markov networks. In: Proceedings of GECCO 2007: Genetic and Evolutionary Computation Conference, Companion Material. GECCO '07 Proceedings of the 9th annual conference companion on Genetic and evolutionary computation, London, 07.07.2007-11.07.2007. New York, NY: ACM, pp. 2423-2428. http://dl.acm.org/citation.cfm?id=1274005; https://doi.org/10.1145/1274000.1274005
Conference Proceeding
Statistical optimisation and tuning of GA factors
Petrovski A, Brownlee A & McCall J (2005) Statistical optimisation and tuning of GA factors. In: The 2005 IEEE Congress on Evolutionary Computation, 2005. The 2005 IEEE Congress on Evolutionary Computation, 2005, Edinburgh, Scotland, 02.09.2005-05.09.2005. Piscataway, NJ: IEEE, pp. 758-764. http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=1554759&abstractAccess=no&userType=inst; https://doi.org/10.1109/CEC.2005.1554759
CSCU9A5 - Code Analysis and Performance
ITNPBD6 - data analytics
I also supervise several MSc and final year honours dissertation projects. If you have an idea for a project related to one of my research areas, please get in touch.
Also Programme Director for the AI and Big Data MSc degrees. Please email artificial-intelligence@stir.ac.uk or big-data@stir.ac.uk if you have queries regarding those.
