Dr Andrew Colligan

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 QUB Research Portal using 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 Imelda Friel at the MadeAI Conference in Porto, Portugal in July 2024.

This paper examines the application of a novel Evolutionary-Development (Evo-Devo)
system that integrates AI tools within the conceptual design process to produce populations of
innovative design options. The aim is to allow the behaviours of designs to be learned and then
exploited later in the design process. Here a design concept (referred to as an organism) is
constructed from cells, which have an evolving NN architecture controlling each
cells’parameterisation. The following work demonstrates the application of the Evo-Devo
process on a volume-to-point heat transfer problem, returning design concepts with a network
of heat channels that direct heat built up in the plate to a point at ambient temperature

This paper was presented by Professor Andy Tyrrell on behalf of our Alumni, Rahul Dubey at the July 2024 IEEE sponsored World Congress on Computational Intelligence from June 30 – July 5, in Yokohama, Japan Conference

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 Computational Intelligence from June 30 – July 5, in Yokohama, Japan Conference

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 Computational Intelligence, 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.

This paper was presented at the 2023 3rd International Conference on Mechanical, Aerospace and Automotive Engineering (CMAAE 2023) held in Nanjing, China.

A buffer is an important element in a production line and its allocation influences the throughput and inventory of the line. The buffer allocation problem can be framed as a multi-objective optimisation problem and is often addressed by using meta-heuristic algorithms, such as evolutionary algorithms. However, these algorithms primarily focus on the “genetic evolution” aspect and do not take in to account the impact of the biological “organism development” process, potentially constraining the exploration of the solution space. In this paper, a bio-inspired evolution-development (evo-devo) approach for modelling and optimising buffer allocations is proposed. The organism representing a production line is defined and modelled, and the evolution and development processes of organisms are developed for researching optimised solutions. The method has been validated by a simulation of a buffer allocation optimisation in an unreliable serial production line with multi-objectives, aiming to maximize production throughput and minimize the total buffer size. Results show that the
proposed method can efficiently obtain solutions, while also achieving greater exploration of the solution space than competing evolutionary algorithms such as the Non-Dominated Sorting Genetic Algorithm II. The proposed approach’s functionality means that it could be applied to other areas of generative design of future factories.

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 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?

Supporting presentation to the Evolving Design Modifiers paper

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.