Evo-Devo Strategies for Generative Architecture: Colour-Based Patterns in Polygon Meshes

Navarro-Mateu, Diego; Cocho-Bermejo, Ana

doi:10.3390/biomimetics5020023

Cited by 5 publications

(2 citation statements)

References 32 publications

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“…While proving successful this process proved to be slow and computationally expensive in order to find the optimal solution in the fitness landscape, and a question of scalability is raised. Generative design [3] has been used, mainly for theoretical studies and seldom for real engineering structures, alongside evolutionary techniques to reduce time and make fitness landscape exploration more efficient [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Investigation of starting conditions in generative processes for the design of engineering structures

Buchanan,

Dubey,

Hickinbotham

et al. 2023

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

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Engineering design has traditionally involved human engineers manually creating and iterating on designs based on their expertise and knowledge. Bio-inspired Evolutionary Development (EvoDevo) generative algorithms aim 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 solution (seed) which the development process will optimize. The question is will a relatively good starting seed always yield a good set of design solutions. 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 suboptimal 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.

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Section: Introductionmentioning

confidence: 99%

Investigation of starting conditions in generative processes for the design of engineering structures

Buchanan,

Dubey,

Hickinbotham

et al. 2023

2023 IEEE Symposium Series on Computational Intelligence (SSCI)

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“…These aspects are key when applying the methodology to real engineering design problems, because evolution is needed to discover and fine tune the appropriate developmental strategy on the growing design as it interacts with its environment. Navarro et al discuss the challenges of implementing EvoDevo for design problems [12]. They use the Devo concept to drive the production of a wide range of coloured shapes, but whilst the process is efficient, an evolutionary component has not yet been implemented.…”

Section: Introductionmentioning

confidence: 99%

Evolving Design Modifiers

Hickinbotham

Dubey

Friel³

et al. 2022

2022 IEEE Symposium Series on Computational Intelligence (SSCI)

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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.

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