Representing Design Problems for Optimisation

The application of parametric methods based on fundamental principles for definition of parametric geometry [Jin and Sendhoff 2009] must reflect specific standards to be applied to shape generation [S´obester 2009]; and besides robustness and flexibility, conciseness is an important characteristic of the encoded geometry, since the number of parameters influences the dimensionality of the search space. Increasing the ranges of parameters dur- ing definition of geometries increases the potential for innovative design solutionsCarrese

[2012, p. 14]. Flexibility of representation in parametric design is limited to the parame- ter definition, while emergent representations allow for changes of topology and structure of the geometry during optimisation. The use of genetic programming to introduce flex- ible computational representations can increase the variety in generated geometry and enhance the innovative capacity of optimisation processes. Emergent representations and representational flexibility are requirements for a morphological search tool that supports design decisions in the early stages of architectural design.

Increasing the synergistic effects between AI systems and human intelligence demands a shared decision making space, or a so-called ‘state space’, in which different states of computational architectural models are linked by meaningful transformations. In AI, only what can be represented is perceived as real. Therefore, for using intelligent design systems, we need to describe architectural design intent in feasible computational rep- resentations for the assessment by a search algorithm. The complexity of architectural design is reflected in the complexity of the representation chosen for the description of the design problem.

To facilitate this process, the definition of the architectural genotype (representa- tion) 28 needs to encode the largest design space possible for the generated phenotype

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(architectural geometry) during architectural optimisation processes. Here, the advan- tage of genetic programming in providing flexible representations allows one to transcend the possibilities of state-of-the-art optimisation using static representations. An adaptive definition of geometry opens up the potential for combining architectural sub-systems with varying levels of detail in response to the changing focus of computational design processes.

The selection of a feasible output of the computational design process and remod- elling of those solutions using other computational tools to reach the next design state is common practice in contemporary digital design. A generative interpretation of this con- cept reveals the need for representational continuity between different design states. At least the geometric representation between two different GD processes needs to be shared to facilitate the use of an output geometry of one as an input geometry for the other. Be- sides the 2D representation commonly used in shape grammar, a mesh representation is a feasible geometric representation that might be used to facilitate the switching between multiple GD tasks [Muehlbauer et al. 2017a]. Mesh representations used for case-specific and site specific design can adapt to the cognitive level of the design task, and offer the possibility for geometric adaptation to the control of the designer. I shall return to the discussion about the need for the designer to control the design process in Chapter4. For now, I want to turn to the discussion about the trade-offs that emerge during multi-criteria optimisation in architectural design.

The division of optimisation processes as described by Radford and Gero [Radford and Gero 1988] into “generation”, “simulation” and “objectives” reveals its inherent com- plexity. The discussion about the definition of the design seeds [Frazer 1995] and body plans [DeLanda 2002] as encoding strategies for architectural design is closely linked to the generational capabilities of GD algorithms. The choice of algorithm and computational representation to describe design solutions is crucial to institute a meaningful form-making process. The shortcomings of computational representations in describing “a series of sub- systems, all interlinked, yet sufficiently free of one another to adjust independently in a feasible amount of time” Alexander[1964, p. 43], can be addressed by introducing multi- staged and multi-scale representations. These representations allow for the transfer from one design state to another in a multi-staged model and the simultaneous optimisation of global and local aspects of system in multi-scale models. Linking sub-systems to complex computational representations poses challenges to the user evaluation of design solutions. The level of detail in the computational representation for choice-based user evaluation

the generational capacity, restricting the interactive potential while expanding the design space. Most machine learning algorithms and artificial neural networks are perceived as black boxes based on their computational complexity. Mediating and balancing the aspects of interactive potential and encoding the proper extent of the design space are central in the design of transparent search strategies. With respect to the exploitation potential of these processes in interactive optimisation, the representation needs to be chosen carefully for specific design cases. Also the choice of machine learning algorithms needs to be undertaken in a thoughtful manner to ensure the possibility to extract internalised rules easily.

needs to be considered carefully.