Our Team > Our Researchers > Dr Edgar Buchanan Berumen

Dr Edgar Buchanan Berumen

Our Researchers

Research Fellow

University of York

Edgar completed his PhD in 2019 from the University of York in the Department of Electronics. His PhD focused upon the fault-tolerance in foraging robotics swarms. After completing his PhD, Edgar worked as Research Associate in the Autonomous Robotic Evolution project  which consisted on the autonomous design and fabrication of robots. His research interests are in the areas of bio-inspired computation and robotics including swarm intelligence and robotics, evolutionary computation and robotics and, biomimicry robotics. Edgar is also interested in autonomous design and fabrication of structures.

RIED Specific Links & Papers

  • Investigation of starting conditions in generative processes for the design of engineering structures (December 2023)

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

    Engineering design has traditionally involved human engineers manually creating and iterating on designs based on their expertise and knowledge. In Bio-inspired Evolutionary Development (EvoDevo), generative algorithms are used to explore a much larger design space that may not have ever been considered by human engineers. However, for complex systems, the designer is often required to start the EvoDevo process with an initial design (seed) which the development process will optimise. The question is: will a good starting seed yield a good set of design solutions for the given problem? This paper considers this question and suggests that sub-optimal seeds can provide, up to certain limits, better design solutions than relatively more optimal seeds. In addition, this paper highlights the importance of designing the appropriate seed for the appropriate problem. In this paper, the problem analysed is the structural performance of a Warren Truss (bridge-like structure) under a single load. The main conclusion of this paper is that up to a limit sub optimal seeds provide in general better sets of solutions than more optimal seeds. After this limit, the performance of sub-optimal seed starts to degrade as parts of the phenotype landscape become inaccessible.