Dr Sandy Brownlee

Senior Lecturer in Computing Science

Computing Science and Mathematics - Division Room 4B125 University of Stirling, Stirling, FK9 4LA

Dr Sandy Brownlee

Contact details

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http://www.stir.ac.uk/people/alexanderbrownlee

About me

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.

Research (8)

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.  

Projects

AirOpt - optimisation of airspace to save 2MT CO2 a year
PI: Dr Sandy Brownlee
Funded by: Innovate UK
–

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
–

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
–

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
–

Crowd-sourcing the aural identities of places by evolutionary optimisation
PI: Dr Sandy Brownlee
Funded by: Scottish Crucible
–

Outputs (100)

Outputs

Conference Paper (published)

Explaining a Staff Rostering Problem using Partial Solutions

Catalano GAPI, Brownlee A, Cairns D, McCall J, Fyvie M & Ainslie R (2024) Explaining a Staff Rostering Problem using Partial Solutions. In: TBC. Lecture Notes in Artificial Intelligence. AI-2024 Forty-fourth SGAI International Conference on Artificial Intelligence, Cambridge, 17.12.2024-19.12.2024. Cham, Switzerland: Springer.

Conference Paper (published)

Unexplained Fluctuations in Particle Swarm Optimisation Performance with Increasing Problem Dimensionality

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.

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

Conference Paper (published)

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 Paper (published)

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.

Conference Paper (published)

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.

Article

Metaheuristics "In the Large"

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 Paper (published)

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 Paper (published)

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 Paper (published)

Towards explaining metaheuristic solution quality by data mining surrogate fitness models for importance of variables

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

Conference Paper (published)

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

Conference Paper (published)

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 Paper (published)

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 Paper (published)

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

Conference Paper (published)

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 Paper (published)

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 Paper (published)

Mutual Information Iterated Local Search: A Wrapper-Filter Hybrid for Feature Selection in Brain Computer Interfaces

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 Paper (published)

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 Paper (published)

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 Paper (published)

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

Conference Paper (published)

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

Conference Paper (published)

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 Paper (published)

Connecting automatic parameter tuning, genetic programming as a hyper-heuristic and genetic improvement programming

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 Paper (published)

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 Paper (published)

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

Conference Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

Embedded Dynamic Improvement

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 (published)

Multi-objective optimization for a large scale retrofit program for the housing stock in the North East of England

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. In: volume 78. 6th International Building Physics Conference. Amsterdam: Elsevier, pp. 854-859. http://www.sciencedirect.com/science/article/pii/S1876610215017397; https://doi.org/10.1016/j.egypro.2015.11.007

Conference Paper (published)

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

Conference Paper (published)

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

Conference Paper (published)

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 Paper (published)

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

Presentation / Talk

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. 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 Paper (published)

A Comparison of Approaches to Stepwise Regression Analysis for Variables Sensitivity Measurements Used with a Multi-Objective Optimization Problem

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 Paper (published)

Applying Global And Local SA In Identification Of Variables Importance With The Use Of Multi-Objective Optimization

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 Paper (published)

A comparison of approaches to stepwise regression for global sensitivity analysis used with evolutionary optimization

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

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 Paper (published)

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

Conference Paper (published)

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 Paper (published)

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

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 Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

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 Paper (published)

Approaches to selection and their effect on fitness modelling in an Estimation of Distribution Algorithm

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 Paper (published)

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 Paper (published)

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 Paper (published)

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

Teaching

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.

Research programmes

Research centres/groups

Research themes