Photo of Prof Jonathan Fieldsend

Prof Jonathan Fieldsend

Professor in Computational Intelligence


Location: Innovation Centre Phase 1 1

Telephone: 01392 722090

Extension: (Streatham) 2090

Office: Innovation Centre Phase 1, Room 1


I am Professor of Computational Intelligence, Director of Research & Impact, and Academic Lead of the Optimisation Group in the Department of Computer Science. I am also Turing Fellow.

I graduated with a BA in Economics from the University of Durham in 1998, an MSc in Computational Intelligence from the University of Plymouth in 1999 and a PhD in Computer Science from the University of Exeter in 2003. Following which I held postdoctoral research positions before starting as Lecturer at Exeter in 2006.

My research has been supported by a number of grants, with funders including EPSRC, Innovate UK, NERC, and industy. I am currently an Associate Editor of ACM Transactions on Evolutionary Learning and Optimization and IEEE Transactions on Evolutionary Computation. I am a vice-chair of the IEEE Computational Intelligence Society Task Force on Data-Driven Evolutionary Optimization of Expensive Problems and also vice-chair of the IEEE Computational Intelligence Society Task Force on Multi-Modal Optimization. I was also co-Chair of the EMO Track at GECCO 2019 and GECCO 2020, and Editor-in-Chief of GECCO 2022.

Most of my codebase relating to my recent publications is available on GitHub. Please access the repositories here.

Research and Professional Interests

My main areas of research are: developing multi-objective objective optimisation methods, multi-modal optimisation, optimisation with uncertainty, evolutionary approaches to learning, data visualisation, as well as the use of Bayesian classification/modelling techniques.

Previous industrial projects I've worked on include:

  • Automatic Coverage and Capacity Optimisation for Next Generation Access Technology (with Motorola),
  • Optimisation of Fraud Detection Software (with AI corp), 
  • Automated Multi-Objective Optimisation of Short Term Alert Safety Net Systems (with NATS)
  • Multi-Objective Optimisation of Wireless Mesh Networks (including resource allocation, and interactive visualisation tools) with IMC group.
  • Data-Driven Surrogate-Assited Fluid Dynamic Optimisation, which involved collaboration with the UK Aerospace Technology Institute and QinetiQ on complex aerodynamic optimisation, with Hydro International on cyclone separation and with Ricardo on diesel particle tracking.
  • MASS modelling and optimisation (with the MET office)
  • Calibration of digital twin models for buildings and road networks (with City Science and Hoare Lea)
  • Human centric buildings for a carbon neutral future (with City Science)

Current industry projects include:

  • Bayesian optimisation for product design (with Hydro International) 
  • Computational modelling and optimisation of plasma processes (with Oxford Instruments Plasma Technology)

Citation stats: h-index: 27 and i10-index: 58 according to Google scholar

Open Positions

I do not currently have any open post-doctoral posititions in my group, but I welcome contact from potential PhD students who want to persue a programme of research in the areas of multi-objective optimisation, evolutionary computation and machine learning.


I am coordinator and lecturer of the following module:

COMM510 Multi-Objective Optimisation and Decision Making

I am interested in supervising postgraduate students with projects in the broad area of Nature Inspired Computation and Machine Learning. Please apply via the university online portal.

Recent news


November 2022 - Joined the programme committee of GECCO 2023

October 2022 - Presented work on using the ND-Tree for maintaining an active archive (source of parents) at EA2022

October 2022 - Invited as a participant at the workshop on Multiobjective Optimization on a Budget at Schloss Dagstuhl – Leibniz-Zentrum für Informatik in Germany, September 2023

September 2022 - Joined programme comittee of EMO 2023

July 2022 - GECCO 2022: first hybrid GECCO, and largest number of attendees of any GECCO so far (just shy of 1000). Benefited greatly from a fantastic team!

October 2021 - Appointed as Turing Fellow for the coming year

April 2021 - "Multi-objective Bayesian optimisation using an exploitative attainment front acquisition function" accepted for as a full paper in IEEE Congress on Evolutionary Computation, with Finley Gibson and Richard Everson

April 2021 - "Towards Population-based Fitness Landscape Analysis Using Local Optima Networks" accepted for as a full paper in the Landscape Aware Heuristic Search Workshop of ACM GECCO, with Melike Karatas and Ozgur Akman

April 2021 - "How Bayesian Should Bayesian Optimisation Be?" accepted as a full paper to the SAEOpt Workshop in ACM GECCO, with Geaorge De Ath and Richard Everson

Feb 2021 - Joined the Program Committee of the MCDM symposium at IEEE SSCI 2021

Feb 2021 - Joined the Program Commitee of IEEE CEC 2021

Feb 2021 - Appointed as an Associate Editor of IEEE Transactions on Evolutionary Computation

Dec 2020 - Joined the Program Comittee of GECCO 2021

Dec 2020 - SAEOpt Workshop confirmed for GECCO 2021, co-organising with Richard Everson (Exeter), Alma Rahat (Swansea), Yaochu Jin (Surrey) and Handing Wang (Xidian) 

Dec 2020 - EAPwU Workshop confirmed for GECCO 2021, co-organising with Khulood Alyahya (Exeter), Tinkle Chugh (Exerter) and Juergen Branke (Warwick)

Dec 2020 - Competition on Niching Methods for Multimodal Optimization confirmed for GECCO 2021, co-organising with Michael Epitropakis (Lancaster), Xiaodong Li (RMIT) and Mike Preuss (Lieden)

Nov 2020 - Non-dominated Sorting on Performance Indicators for Evolutionary Many-objective Optimization, by Hao Wang, Chaoli Sun, Guochen Zhang, Jonathan Fieldsend and Yaochu Jin, accepted for publication in Information Sciences. 


Qualifications: BA Economics (Dunelm), MSc Computational Intelligence (Plym), PhD Computer Science (Exon)

Professional memberships: MIEEE, FHEA

Patents: A method of selecting operational parameters in a communication network EP1730980 WO2005091948 GB2412275