# EvoDevo: Bioinspired Generative Design via Evolutionary Graph-Based Development
## 1 Introduction  
In traditional design processes, a designer will take a project brief or specification and use their expert knowledge to construct a solution and then test it to see whether it meets the set of requirements. There is normally an iterative process between the design and analysis stages before the part is sent to manufacturing. As a result of time, cost and resource constraints, this approach can lead to design fixation or non-value-adding tasks [1, 2]. Generative design (GD) looks to address these issues via automation. Generally, it can be summed up as a series of tools that use advanced algorithms and artificial intelligence to create designs for a given set of criteria. This involves the exploration of a vast number of design options, presenting a range of design solutions to the user for selection [3]. The design solutions that emerge may be different from the general best practice or from what may be possible to discover using manual design processes. Some GD systems consist of both an expert system and an optimisation system [4]. The expert system emulates the expert's knowledge and ability to generate and critique solutions to a specific design problem. An optimisation system, however, improves designs with respect to some objective criteria. Unlike traditional optimisation, where objectives relate to weight or cost, for example, GD systems also optimise other criteria such as aesthetics or manufacturability. The optimisation task may not be concerned with discovering global optima; instead, a multi-modal optimisation task can be performed to find multiple solutions to a problem. Commonly, topology optimisation acts as an engine to improve the design of a pre-defined shape. GD not only deals with topology optimisation but also integrates other techniques such as shape and size optimisation. This integration of different optimisation techniques can often be a limitation to interoperability between the geometric representations required for topology and shape/size optimisation. Generally, topology optimisation utilizes the finite element method (FEM), where the design space is discretised into simple primitives called _elements_. This discretised geometry is discontinuous and can be difficult to map back to the parametric computer-aided design (CAD) model (i.e., boundary representation (B-Rep)) used for shape optimisation [4]. There can also be a high computational cost associated with generating and analysing the large number of designs required for a GD system. For small-to-medium-sized businesses with no access to high-performance computing, this could represent a barrier to adoption. Bioinspired GD systems apply features of biological systems to the design creation process [5]. In this paper, inspiration is taken from the biological concept of evolutionary development (EvoDevo) to evolve development rules that can be used to generate design solutions for different engineering problems using evolutionary algorithms [6]. This can be thought of as \"evolving the designer, not the design\". By encoding the development rules in the genome of the evolutionary algorithm instead of in the design itself, a richer exploration of the design space is possible. In this paper, this is applied to the automated design of truss bridge structures, a popular benchmark in structural optimisation [7, 8]. In EvoDevo, an initial design is divided into basic units called _cells_, each containing an identical copy of an artificial gene regulatory network (GRN) model. This GRN model governs the growth of the structure through a set of developmental rules applied to each cell. The GRN model itself is encoded in a genome, which is optimised using an evolutionary algorithm. Learning from graph-structured data using graph neural networks (GNNs) offers a promising approach, as GRN models reason over graphs, with trusses being naturally representable in this format. However, the \"black-box\" nature of neural network outputs can make interpretation difficult.