Our Team > Our Researchers > Dr Simon Hickinbotham

Dr Simon Hickinbotham

Our Researchers

Research Fellow

University of York

Simon studied Ecology at Bachelors level at RHBNC London, before moving to York to complete an MSc in Biological Computation in 1994. His research project led to an EPSRC-funded position as a research associate in computer vision, which was the subject of his PhD thesis completed in 2000 in Computer Science. Simon was managing director of Kickstone Technologies Limited from 2000-2005, specialising in pattern recognition in the biotech sector. Since then he has worked on a wide range of projects in evolutionary computing at the University of York, with applications in robotics, healthcare and mass spectrometry analysis.

His research interests are in the areas of self-organisation in artificial life and evolutionary computing. He developed an artificial chemistry called Stringmol to study how an evolving system can organise itself free from externally imposed constraints. In addition to this fundamental research, he enjoys using advanced pattern recognition to solve real-world engineering problems such as power station process management and feature detection in medical applications.

RIED Specific Links & Papers

  • Evolving Design Modifiers (December 2022)

    Evolutionary Developmental biology (EvoDevo) is a process of directed growth whose mechanisms could be used in an evolutionary algorithm for engineering applications. Engineering design can be thought of as a search through a high-dimensional design space for a small number of solutions that are optimal by various metrics. Configuring this search within an EvoDevo algorithm may allow developmental processes to provide a facility to give more immediate, localised feedback to the system as it grows into its final optimal configuration (form). This approach would augment current design practices. The main components needed to run EvoDevo for engineering design are set out in this paper, and these are developed into an algorithm for initial investigations, resulting in evolved neural network-based structural design modifying operators that optimise the structure of a planar truss in an iterative, decentralized manner against multiple objectives. Preliminary results are presented which show that the two levels feedback at the Evo and Devo stages drive the system to ultimately produce feasible solutions.

  • Framework Training Interview (November 2022)

    Queen’s University Belfast & University of York – RIED: ReImagining Engineering Design

    Framework Training supported Queen’s University Belfast & University of York with specialist training on best practices for software development to support a new interdisciplinary project called RIED: ReImagining Engineering Design – to drive the generation of new methods for design engineering.

  • Local Fitness Landscape Exploration Based Genetic Algorithms (January 2023)

    Genetic algorithms (GAs) have been used to evolve optimal/sub-optimal solutions of many problems. When using GAs for evolving solutions, often fitness evaluation is the most computationally expensive, and this discourages researchers from applying GAs for computationally challenging problems. This paper presents an approach for generating offspring based on a local fitness landscape exploration to increase the speed of the search for optimal/sub-optimal solutions and to evolve better fitness solutions. The proposed algorithm, “Fitness Landscape Exploration based Genetic Algorithm” (FLEX-GA) can be applied to single and multi-objective optimization problems. Experiments were conducted on several single and multi-objective benchmark problems with and without constraints. The performance of the FLEX-based algorithm on single-objective problems is compared with a canonical GA and other algorithms. For multi-objective benchmark problems, the comparison is made with NSGA-II, and other multi-objective optimization algorithms. Lastly, Pareto solutions are evolved on eight real-world multi-objective optimization problems, and a comparative performance is presented with NSGA-II. Experimental results show that using FLEX on most of the single and multi-objective problems, the speed of the search improves up to 50% and the quality of solutions also improves. These results provide sufficient evidence of the applicability of fitness landscape approximation-based algorithms for solving real-world optimization problems.

  • Clare Newton & RIED (June 2023)

    RIED is inspired by nature. we are observing natural systems at work and codifying these into engineering systems to produce innovative designs and processes. Nature also inspires art. Our great friend and outstanding photographic artist Clare Newton has produced an amazing exhibition where art and technology and nature are brought together. The Seeds of Change project explores the nature around us and gives alternative perspectives to the amazing world of engineering design that is being created in RIED.

    Exposing innovative science to the public is very important for me. The reason I made this a big part of the Seeds of Change project, is that I have not seen the inside story of science before and in particular in a non-scientific context.  
        Although there are science museums, they look at subjects that are around us, to enable children to easily relate to a subject.  There is very few informative science projects that are portrayed in local exhibition form, and in a non-scientific public place. The purpose of this project is to reach out to a different sector of audiences and bring an unusual aspect of science that they can be inspired by and make the imagery immersive, so that the viewer feels the same excitement as I did when shooting the project.
        Over the years, I have brought different worlds together to widen a viewer’s point of view. This project I reach out to the non-scientific communities in public gardens, nature museums, churches, and other resourceful places. I know the public enjoy something different and will become fascinated by what I uncover.”

    Clare’s exhibition can be viewed from the 6th June to 28th August 2023 in:

    Gilbert White’s House,

    The Wakes,

    High Street,



    GU34 3JH,

    T: 01420 511275,

    Open daily Tuesday to Sunday 10:30am to 4:30pm

    QR code to Seeds of Change
  • Theory of Evolutionary Systems Engineering (December 2023)

    This paper was presented at the 2023 IEEE Symposium Series on Computational Intelligence (SSCI in Mexico City, Mexico from 5-8 December 2023.

    Evolutionary approaches to engineering design involve generating populations of candidate solutions that compete via a selection process iteratively, to improve measures of performance over many generations. Although the attractive properties of biological evolutionary systems have motivated researchers to investigate emulating them for engineering design, there has been an emphasis on using encodings of the technical artefacts themselves, rather than encoding a complete bio-inspired system which is capable of producing such artefacts. It is the latter approach which is the subject of this contribution: how might a bio-inspired system be designed that self-organises the process of engineering design and manufacture?