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Computer Science

D-Index
54
Citations
12317
World Ranking
4537
National Ranking
275

Overview

Jon Timmis is affiliated with the University of Sunderland in the United Kingdom. Their research spans multiple domains within computer science and engineering, with a focus on artificial intelligence, mechanical engineering, software, and immunology. Their scholarly contributions cover a range of topics including modular robots and swarm intelligence, evolutionary algorithms and applications, reinforcement learning in robotics, metaheuristic optimization algorithms, and immunological studies related to T-cell and B-cell functionality.

The scientist's recent publications demonstrate a varied research interest that intersects robotics, artificial intelligence, and immunology. Some notable papers include:

  • B cell zone reticular cell microenvironments shape CXCL13 gradient formation (2020, Nature Communications)
  • Assessing ranking and effectiveness of evolutionary algorithm hyperparameters using global sensitivity analysis methodologies (2022, Swarm and Evolutionary Computation)
  • Morpho Evolution With Learning Using a Controller Archive as an Inheritance Mechanism (2022, IEEE Transactions on Cognitive and Developmental Systems)
  • Bootstrapping Artificial Evolution to Design Robots for Autonomous Fabrication (2020, Robotics)
  • Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces (2023, IEEE Transactions on Evolutionary Computation)

Timmis has published extensively in several venues, with a concentration in robotics and artificial intelligence focused journals and conferences. Frequently appearing publication venues include:

  • Robotics
  • Frontiers in Robotics and AI
  • Software & Systems Modeling
  • arXiv (Cornell University)
  • Nature Communications

The scientist has collaborated regularly with several co-authors over their career. Frequent collaborators include Andy M. Tyrrell, Edgar Buchanan, Emma Hart, A. E. Eiben, and Alan Winfield. These collaborative efforts reflect interdisciplinary work at the intersection of AI, evolutionary computation, and robotics.

Jon Timmis's expertise is demonstrated through their contributions to core fields such as computer science and engineering, with notable specialization in:

  • Artificial Intelligence
  • Mechanical Engineering
  • Software
  • Immunology
  • Computational Theory and Mathematics

The main research themes in their work include:

  • Modular Robots and Swarm Intelligence
  • Evolutionary Algorithms and Applications
  • Reinforcement Learning in Robotics
  • Metaheuristic Optimization Algorithms Research
  • T-cell and B-cell Immunology
  • Immunotherapy and Immune Responses
  • Chemokine receptors and signaling

Best Publications

  • An artificial immune network for multimodal function optimization

    L.N. de Castro;J. Timmis

  • Artificial immune systems as a novel soft computing paradigm

    L. N. De Castro;J. I. Timmis

  • An artificial immune system for data analysis

    Jon Timmis;Mark Neal;John Hunt

  • Artificial Immune Recognition System (AIRS): An Immune-Inspired Supervised Learning Algorithm

    Andrew Watkins;Jon Timmis;Lois Boggess

  • Theoretical advances in artificial immune systems

    J. Timmis;A. Hone;T. Stibor;E. Clark

  • Artificial Immune Systems: A Novel Approach to Pattern Recognition

    Leandro N. de Castro;Jon Timmis

  • An Immune Algorithm for Protein Structure Prediction on Lattice Models

    V. Cutello;G. Nicosia;M. Pavone;J. Timmis

  • Negative Selection: How to Generate Detectors

    Modupe Ayara;Jon Timmis;Rogério de Lemos;Leandro N. de Castro

  • Immune inspired somatic contiguous hypermutation for function optimisation

    Johnny Kelsey;Jon Timmis

  • Artificial immune systems---today and tomorrow

    Jon Timmis

  • Artificial Immune Systems: A New Computational Approach

    Leandro N. de Castro;Jon Timmis

  • Revisiting the Foundations of Artificial Immune Systems for Data Mining

    A.A. Freitas;J. Timmis

  • Artificial Immune Recognition System (AIRS): Revisions and Refinements

    Andrew Watkins;Jon Timmis

  • An Overview of Artificial Immune Systems

    J. Timmis;T. Knight;L. N. de Castro;E. Hart

  • AISEC: an artificial immune system for e-mail classification

    A. Secker;A.A. Freitas;J. Timmis

  • CoCoRo -- The Self-Aware Underwater Swarm

    Thomas Schmickl;Ronald Thenius;Christoph Moslinger;Jon Timmis

  • Towards a Conceptual Framework for Artificial Immune Systems

    Susan Stepney;Robert E. Smith;Jonathan Timmis;Andrew M. Tyrrell

  • Journeys in non-classical computation I: A grand challenge for computing research

    Susan Stepney;Samuel L. Braunstein;John A. Clark;Andrew M. Tyrrell

  • On the hierarchical classification of G protein-coupled receptors

    Matthew N. Davies;Andrew Secker;Alex A. Freitas;Miguel Mendao

  • Revisiting the Foundations of Artificial Immune Systems: A Problem-Oriented Perspective

    Alex Alves Freitas;Jon Timmis

  • Application areas of AIS: the past, present and future.

    Emma Hart;Jon Timmis

Frequent Co-Authors

Andy M. Tyrrell
Andy M. Tyrrell University of York
Alex A. Freitas
Alex A. Freitas University of Kent
Alan F. T. Winfield
Alan F. T. Winfield University of the West of England
Darren R. Flower
Darren R. Flower Aston University
Peter J. Bentley
Peter J. Bentley University College London
Vipin Kumar
Vipin Kumar University of California, San Diego
Susan Stepney
Susan Stepney University of York
A. E. Eiben
A. E. Eiben Vrije Universiteit Amsterdam
Paul M. Kaye
Paul M. Kaye Hull York Medical School
Leandro Nunes de Castro
Leandro Nunes de Castro Florida Gulf Coast University

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