Dr Edgar Buchanan Berumen

The Univeristy of York team will be taking part in the UK Robotics LiveStream on Wednesday 21st June at 4pm, you can watch the demonstrastion LIVE here – https://www.youtube.com/live/Wf29_bbNlko?feature=share

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.

This presentation was given by Dr Edgar Buchanan Berumen, a member of our RIED Team from the University of York, to the IEEE sponsored World Congress on Evolutionary Computation, one of three flagship conferences at the IEEE WCCI 2024, from June 30 – July 5, in Yokohama, Japan.

Other RIED team members including Simon Hickinbotham, Rahul Dubey, Imelda Field, Andrew Colligan, Mark Price and Andy Tyrrell contributed to the paper.

Simon Hickinbotham, a Research Fellow from the RIED team at the University of York, presented this Late Breaking Abstract and Poster at the International Society for Artificial Life 22-26 July 2024 Conference in Copenhagen.

Published in the Artificial Life Journal and accessible via Evolving Novel Gene Regulatory Networks for Structural Engineering Designs | Artificial Life | MIT Press and via the “View” panel.

Abstract: Engineering design optimization poses a significant challenge, usually requiring human expertise to discover superior solutions. Although various search techniques have been employed to generate diverse designs, their effectiveness is often limited by problem-specific parameter tuning, making them less generalizable and scalable. This article introduces a framework inspired by
evolutionary and developmental (evo-devo) concepts, aiming to automate the evolution of structural engineering designs. In biological systems, evo-devo governs the growth of single-cell organisms into multicellular organisms through the use of gene regulatory networks (GRNs). GRNs are inherently complex and highly nonlinear, and this article explores the use of neural networks and genetic programming as artificial representations of GRNs to emulate such behaviors. To evolve a wide range of Pareto fronts for artificial GRNs, this article introduces a new technique, a real value–encoded neuroevolutionary method termed real-encoded NEAT (RNEAT). The performance of RNEAT is compared with that of two well-known evolutionary search techniques across different 2-D and 3-D problems. The experimental results demonstrate two key findings. First, the proposed framework effectively generates a population of GRNs that can produce diverse structures for both 2-D and 3-D problems. Second, the proposed RNEAT algorithm outperforms its competitors on more than 50% of the problems examined. These results validate the proof of concept underlying the proposed evo-devo-based engineering design evolution.

This paper was presented by Professor Andy Tyrrell on behalf of our Alumni, Rahul Dubey at the July 2024 IEEE sponsored World Congress on Evolutionary Computation, one of three flagship conferences at the IEEE WCCI 2024, from June 30 – July 5, in Yokohama, Japan.

In recent years, the evolutionary developmental (Evo-Devo) concept has gained traction in the field of engineering design. This paper presents a new biologically inspired approach rooted in Evo-Devo principles to iteratively develop car chassis designs based on a specified design brief. The proposed method draws inspiration from biological cell growth and differentiation behaviours to generate intricate engineering designs. Employing evolutionary algorithms, the paper aims to evolve gene regulatory
networks that govern the growth of a minimal viable design. The primary goal is to achieve an optimal design capable of withstanding sudden crash impacts within safety limits. Comprehensive simulation results demonstrate that the proposed approach, using genetic algorithms, evolves gene regulatory networks that generate a spectrum of viable designs. Furthermore, the best evolved solution exhibits generalizability and adaptability across different simulation parameters.

This presentation was given by Professor Andy Tyrrell on behalf of our Alumni, Rahul Dubey at the July 2024 IEEE sponsored World Congress on Evolutionary Computation, one of three flagship conferences at the IEEE WCCI 2024, from June 30 – July 5, in Yokohama, Japan.

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?

Dr Simon Hickinbotham from the RIED team, supported by Prof Andy Tyrrell, gave a presentation and took part in a poster session at the 2025 IEEE Symposium Series on Computational Intelligence in Trondheim, Norway 17^th-20^th March 2025 on the subject of Morphogenic Shape Grammars for the Design of Engineering Structures.

The associated poster can be found via the following link

https://riedesign.org/wp-content/uploads/2025/03/Hickinbotham_Poster_Morph_Grammars.pdf

Dr Simon Hickinbotham from the RIED team, supported by Prof Andy Tyrrell, presented this paper at the 2025 IEEE Symposium Series on Computational Intelligence in Trondheim, Norway 17^th-20^th March 2025 on the subject of Morphogenic Shape Grammars for the Design of Engineering Structures. The associated PowerPoint presentation and poster are available in the Library too.

The abstract is as follows

“Bodies of multicellular organisms are laid out according to morphogens: chemical agents which establish a coordinate system in the early embryo and use this to decide where body parts should grow. This process offers a mechanism for the automation of the design of engineering constructs via evolutionary search in a similar manner to the way biological evolution has driven the diversity of body forms of life on Earth. There are many ways of encoding such body plans, but the main existing approaches have problems around managing the complexity and stability of the evolutionary search process, particularly when applied to practical engineering design problems. This contribution takes the notion of morphogen chemical gradients and uses it to develop a novel grammar for shape formation. The central idea is to organise and label the spatial sub-regions of a design before making decisions regarding the finished arrangement of structure. This makes it much simpler to explore compositions of the hierarchy of sub-assemblies in a design, and to represent design of shape in an evolvable manner. A worked example of such a morphogenic shape grammar is described, and used in a multi-objective evolutionary search for optimal bridge truss structures over four fitness objectives with two design constraints. The resulting Pareto front shows a wide variety of bridge designs, demonstrating the power of this approach to generate a diverse set of viable options to meet engineering design challenges.”

This journal paper is published in MDPI Algorithms (Volume 18, Issue 8) and was also selected as the actual Issue cover too accessible via https://www.mdpi.com/1999-4893/18/8.

The paper belongs to the Special Issue Data-Driven Intelligent Modeling and Optimization Algorithms for Industrial Processes: 2nd Edition).

Abstract: Automated generative design is increasingly used across engineering disciplines to accelerate innovation and reduce costs. Generative design offers the prospect of simplifying manual design tasks by exploring the efficacy of solutions automatically. However, existing generative design frameworks rely heavily on expensive optimisation procedures and often produce customised solutions, lacking reusable generative rules that transfer across different problems. This work presents a bioinspired generative design algorithm utilizing the concept of evolutionary development (EvoDevo). This evolves a set of developmental rules that can be applied to different engineering problems to rapidly develop designs without the need to run full optimisation procedures. In this approach, an initial design is decomposed into simple entities called cells, which independently control their local growth over a development cycle. In biology, the growth of cells is governed by a gene regulatory network (GRN), but there is no single widely accepted model for this in artificial systems. The GRN responds to the state of the cell induced by external stimuli in its environment, which, in this application, is the loading regime on a bridge truss structure (but can be generalized to any engineering structure). Two GRN models are investigated: graph neural network (GNN) and graph-based Cartesian genetic programming (CGP) models. Both GRN models are evolved using a novel genetic search algorithm for parameter search, which can be re-used for other design problems. It is revealed that the CGP-based method produces results similar to those obtained using the GNN-based methods while offering more interpretability. In this work, it is shown that this EvoDevo approach is able to produce near-optimal truss structures via growth mechanisms such as moving vertices or changing edge features. The technique can be set up to provide design automation for a range of engineering design tasks.