Emma Hart
Emma Hart

Prof Emma Hart

Professor

 0131 455 2783

  E.Hart@napier.ac.uk

  

Biography

Prof. Hart gained a 1st Class Honours Degree in Chemistry from the University of Oxford, followed by an MSc in Artificial Intelligence from the University of Edinburgh. Her PhD, also from the University of Edinburgh, explored the use of immunology as an inspiration for computing, examining a range of techniques applied to optimisation and data classification problems.聽

She moved to 麻豆社区 in 2000 as a lecturer, and was promoted to a Chair in 2008 where she leads a group in Nature-Inspired Intelligent Systems, specialising in optimisation and learning algorithms applied in domains that range from combinatorial optimisation to robotics. Her work mainly involves development of algorithms inspired by biological evolution to discover novel solutions to challenging problems.

She served as Editor-in-Chief of Evolutionary Computation (MIT Press) from 2017.2023. She has been invited to give keynotes at major international conferences including ANTS 2024, IEEE CEC 2022, CLAIO 2020, IEEE CEC 2019, EURO 2016 and UKCI 2015 and was General Chair of PPSN 2016, and as a Track Chair at GECCO for several years. She is an elected member of the Executive Board of the ACM SIG on Evolutionary Computation.

More broadly, she has been an invited member of the UK Operations Research 麻豆社区 Research Panel, in Scotland, she was co-lead of the Artificial Intelligence theme within SICSA for 2 years.. She was appointed as a panel member for REF2021 (UoA11 Computer Science). In 2020 she was appointed to the Steering Committee that developed Scotland's AI Strategy published in 2021 . She has a sustained track record of obtaining funding from the EU, EPSRC and of engaging with industry via KTP projects and consultancy, and participates enthusiastically in public-engagement activity, e.g Pint of Science.

Her work in evolutionary robotics has attracted significant media attention, e.g. in New Scientist, the Guardian, Telegraph and the Conversation. In 2021, she gave a TED Talk on Evolutionary Robotics, available online

In 2022, she was elected as a Fellow of the Royal 麻豆社区 of Edinburgh and in 2023, she received the ACM SIGEVO award for Outstanding Contribution to Evolutionary Computation

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Esteem

Advisory panels and expert committees or witness

  • Scottish Government Steering Committee: AI Strategy
  • Royal 麻豆社区鈥檚 International Networks Committee
  • Elected to the Executive Board of the ACM Special Interest Group SIGEVO
  • Invited to join the IEEE CIS Evolutionary Computation Technical Committee for 2012
  • Appointed to the UK Operational Research 麻豆社区 Research Panel
  • Member of Royal 麻豆社区 delegation of UK scientists in UK-Russia Frontiers of Science Symposium (Kazan)
  • Invited to co-lead SICSA Research Theme on Artificial Intelligence

 

Conference Organising Activity

  • Senior Program Committee Member AAAI-19
  • Track Chair: Complex Systems @ GECCO 2017
  • General Chair of PPSN 2016, Edinburgh
  • Workshop Chair: 鈥 ECAL 2013, 1st Workshop on Fundamentals of Collective Systems, Italy
  • Co-chair and organiser, Dagstuhl Seminar on Artificial Immune Systems, 2011
  • Workshop co-chair: Steering Complex Adaptive Systems, ECAL 2015: 12th International Conference on Artificial Life
  • Workshop Chair, Real-World-Optimisation @ GECCO 2013
  • Workshop Chair: 2nd Awareness workshop at IIEEE SASO 2012 : Challenges for Achieving Self-awareness in Autonomic Systems
  • Technical Chair BioNETICS 2009, Avignon
  • General Chair: 9th International Conference on Artificial Immune Systems at Edinburgh Napier
  • Technical Chair of Bionetics 2011, York, UK
  • Chair of Workshop in Self-Organisation in Pervasive Adaptive Systems, at IEEE SASO 2010, Budapest. 28th September 2010
  • Track Chair: Artificial Immune Systems at GECCO 2014, an ACM SIGEVO Conference.
  • Track Chair: Artificial Immune Systems and Biological and Medical Applications, Genetic & Evolutionary Computation Conference, GECCO 2016
  • Track Chair. Real-World Applications of Optimisation at GECCO 2015 (ACM)
  • Technical chair of IEEE Congress on Evolutionary Computing (CEC), 2009
  • Workshop co-chair: ALIFE 2016, 2nd Workshop on Steering Complex Systems (Cancun, Mexico)
  • Program Chair of the ninth IEEE International Conference on Self-Adaptive and Self- Organizing Systems (2015)
  • Workshop Chair: IEEE SASO, 2nd Workshop on Fundamentals of Collective Systems, London,
  • Workshop Chair: IEEE SASO 2011, 1st Workshop on Self-Awareness in Autonomic Computing USA

 

Editorial Activity

  • Editorial Board: International Journal Metaheuristics
  • Editor-in-Chief of Evolutionary Computation (MIT Press)
  • Guest Editor: Special Issue of J. Evolutionary Intelligence on Aspects of Artificial Immune Systems
  • Editor of the ACM SIGEVO newsletter
  • Guest Editor: Swarm Intelligence: (Special Issue on Artificial Immune Systems) 2010
  • Associate Editor of the Journal of Evolutionary Computation (MITPress)
  • Guest Editor: Scalable Computing: Practice and Experience (Special Issue on Collective Adaptive Systems)

 

Fellowships and Awards

  • Leverhulme Research Fellowship

 

Grant Reviewer

  • Grant reviewer: Carnegie Trust
  • Grant reviewer: Leverhulme Trust
  • Re-elected to EPSRC peer review college

 

Invited Speaker

  • Invited Talk: Tecnologico de Monterrey, Mexico
  • Invited Talk: University of St Andrews (Computer Science)
  • AHDB Smart Agriculture Conference (Invited Speaker)
  • Invited Seminar: University of Aberystwyth: Seminar, Life Long Learning in Optimisation
  • University of Stirling: Research Seminar
  • University of Nottingham Research Seminar
  • Invited Speaker: Women@GECCO workshop, GECCO 2015
  • Keynote Speaker at 28th European Conference on Operational Research in Poznan in July 2016
  • Inivited Talk: Mathematical Modelling of Wind Risk, Arcachon, France. Optimisation for Forestry
  • Keynote: 15th UK Conference on Computational Intelligence (UKCI 鈥2015

 

Media Activity

  • The PerAda project hosted a public debate "Emotion as Interface" at Edinburgh International Science Festival, chaired by Emma Hart, with panel guests Prof Kevin Warwick, Prof Nikola Serbedzija, ad Dr Jenny Tillotson
  • FOCAS project featured in EU FET newsletter

 

Research Degree External Examining

  • PhD Examiner: University of Cardiff (School of Mathematics)
  • PhD Examiner: Robert Gordon University
  • Phd External Examiner Queen Mary University
  • PhD Examiner University of Edinburgh
  • PhD Examiner: University of York
  • PhD Examiner: University of Aberytwyth
  • PhD Examiner: University of Hong Kong
  • PhD Examiner: Manchester Metropolitan
  • PhD examiner, University of Pretoria, South Africa
  • PhD Examiner University of Kent Canterbury
  • PhD Examiner University of YorK (Computer Science)
  • PhD at the University of Cardiff, in the School of Computer Science and Informatics
  • PhD examiner at the University of York
  • PhD examiner, University of Nottingham
  • PhD external examiner at Hong Kong Polytechnic University
  • PhD Examiner: Swinburne University of Technology, Australia

 

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174 results

Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age

Journal Article
Pringle, S., Dallimer, M., Goddard, M. A., Le Goff, L. K., Hart, E., Langdale, S. J., Fisher, J. C., Abad, S.-A., Ancrenaz, M., Angeoletto, F., Auat Cheein, F., Austen, G. E., Bailey, J. J., Baldock, K. C. R., Banin, L. F., Banks-Leite, C., Barau, A. S., Bashyal, R., Bates, A. J., Bicknell, J. E., 鈥avies, Z. G. (2025)
Opportunities and challenges for monitoring terrestrial biodiversity in the robotics age. Nature Ecology & Evolution, 9(6), 1031-1042. https://doi.org/10.1038/s41559-025-02704-9
With biodiversity loss escalating globally, a step change is needed in our capacity to accurately monitor species populations across ecosystems. Robotic and autonomous systems...

XAI for Algorithm Configuration and Selection

Book Chapter
Thomson, S. L., Hart, E., & Renau, Q. (2025)
XAI for Algorithm Configuration and Selection. In N. van Stein, & A. V. Kononova (Eds.), Explainable AI for Evolutionary Computation. Springer. https://doi.org/10.1007/978-981-96-2540-6_6
In this chapter, we consider, formalise, and demonstrate the ways in which XAI can assist or inform algorithm selection and configuration. Reviewing the literature, we notice ...

Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model

Presentation / Conference Contribution
Renau, Q., & Hart, E. (2025, April)
Algorithm Selection with Probing Trajectories: Benchmarking the Choice of Classifier Model. Presented at EvoSTAR 2025, Trieste, Italy
Recent approaches to training algorithm selectors in the black-box optimisation domain have advocated for the use of training data that is 'algorithm-centric' in order to enca...

Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing

Presentation / Conference Contribution
Sim, K., Hart, E., & Renau, Q. (2025, April)
Beyond the Hype: Benchmarking LLM-Evolved Heuristics for Bin Packing. Presented at EvoSTAR 2025, Trieste, Italy
Coupling Large Language Models (LLMs) with Evolutionary Algorithms has recently shown significant promise as a technique to design new heuristics that outperform existing meth...

Into the Black Box: Mining Variable Importance with XAI

Presentation / Conference Contribution
Hunter, K., Thomson, S. L., & Hart, E. (2025, April)
Into the Black Box: Mining Variable Importance with XAI. Presented at Evostar 2025, Trieste, Italy
Recent works have shown that the idea of mining search spaces to train machine learning models can facilitate increasing understanding of variable importance in optimisation p...

Stalling in Space: Attractor Analysis for any Algorithm

Presentation / Conference Contribution
Thomson, S. L., Renau, Q., Vermetten, D., Hart, E., van Stein, N., & Kononova, A. V. (2025, April)
Stalling in Space: Attractor Analysis for any Algorithm. Paper presented at EvoStar 2025, Trieste, Italy
Network-based representations of fitness landscapes have grown in popularity in the past decade; this is probably because of growing interest in explainability for optimisatio...

鈥橞ots on the Ground vs Boots on the Ground: The Future of Robots in Terrestrial Ecological Surveying

Journal Article
White, P., Le Goff, L., Emery, L., Abrahams, C., Findlay, M., Cook, J., Macleod, K., Deacon, L., Reason, P., Stanhope, K., Wale, M., Hart, E., & Diele, K. (2025)
鈥橞ots on the Ground vs Boots on the Ground: The Future of Robots in Terrestrial Ecological Surveying. In Practice (CIEEM), 27, 47-52
At the 2023 CIEEM Modernising Ecology conference, a robot greeted the attendees as they arrived. Was it a glimpse into the future? As with other technologies, robots have the ...

An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation

Presentation / Conference Contribution
Stone, C., Renau, Q., Miguel, I., & Hart, E. (2024, June)
An Evaluation of Domain-agnostic Representations to Enable Multi-task Learning in Combinatorial Optimisation. Presented at 18th Learning and Intelligent Optimization Conference, Ischia, Italy
We address the question of multi-task algorithm selection in combinatorial optimisation domains. This is motivated by a desire to simplify the algorithm-selection pipeline by ...

Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances

Presentation / Conference Contribution
Hart, E., Renau, Q., Sim, K., & Alissa, M. (2024, September)
Evaluating the Robustness of Deep-Learning Algorithm-Selection Models by Evolving Adversarial Instances. Presented at 18th International Conference on Parallel Problem Solving From Nature PPSN 2024, Hagenburg, Austria
Deep neural networks (DNN) are increasingly being used to perform algorithm-selection in combinatorial optimisation domains, particularly as they accommodate input representat...

Identifying Easy Instances to聽Improve Efficiency of聽ML Pipelines for聽Algorithm-Selection

Presentation / Conference Contribution
Renau, Q., & Hart, E. (2024, September)
Identifying Easy Instances to聽Improve Efficiency of聽ML Pipelines for聽Algorithm-Selection. Presented at 18th International Conference, PPSN 2024, Hagenberg, Austria
Algorithm-selection (AS) methods are essential in order to obtain the best performance from a portfolio of solvers over large sets of instances. However, many AS methods rely ...

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