The principles of the system

In the following paragraphs, I list fifteen principles that were developed and argued for in the thesis. These principles should serve as a basis for realizing a functional generative design system.

The core element of the generative design system is the building environment. I believe, that appropriate implementation of this element is a decisive factor in constructing a viable design system. Thus, it is important to define the building environment in an inclusive and balanced way. I consider this principle central. Validity of this principle is supported by the analysis of four prototypes of generative design systems that I undertook in section IX.

PRINCIPLES RELATED TO DESIGN PROCESS

I derived two principles for the generative design system from the discussion about a design process.

1. A building model should be generated in a manner similar to adaptation – a gradual transformation of a building model towards increasing fitness with the building environment (design objectives).

The way a building model is generated is derived from mechanisms of adaptation of complex systems. The generation of a building model is non-linear, involving combination of random search and

‘deterministic’ methods.

2. Beside adaptation, the generative process should involve iteration.

By iteration I understand a cyclical reformulation of the building environment (design objectives) based on data resulting from the generated building model. Iteration stems from a ‘wicked’ nature of architectural problems, where a design problem (design objectives) is defined in a circular way, i.e. the solution affects the problem formulation. The functioning of the generative design system should be based on two mechanisms: adaptation and iteration. The

adaptation should take place within the iteration.

PRINCIPLES RELATED TO THE BUILDING MODEL

3. A generic structure for building models should be flexible. The generative design system can represent only the building models that are anticipated in a predefined generic structure (it cannot represent all conceivable building models). The flexible generic structure for building models would increase their potential diversity. Implication

of the flexible generic structure would be a large search space for the evolutionary algorithm.

4. A building model should consist of elements which resemble real architectural components. That is, building elements should consist of geometric objects and auxiliary information about the kind of architectural component that is represented by the object. The information can include physical properties of architectural components, their function or how they relate to other components.

5. A building model should have a hierarchic structure. Some of its elements should have stronger interconnections than others and they should constitute groups. This would be beneficial in evolution of building models in the following way. The evolution involves mutation and recombination of building models. If selected building elements were strongly interconnected, making groups, then in the adaptation process there would be no need to decompose the adapting models to their basic elements. Because of this, the whole evolutionary process would be faster.

PRINCIPLES RELATED TO THE BUILDING ENVIRONMENT AND BUILDING CHARACTERISTICS

6. The building environment should be inclusive and balanced.

Consequently, the generative design system should be able to consider a number of very different building characteristics, reflecting a typical ability of an architect in the actual design process. The building characteristics that the environment includes should be organized in terms of design constraints and domains they address. The design constraints include: site constraints, building codes, master plan regulations, client’s intentions and architectural qualities. The design domains are drawn from the triple bottom line of sustainable architecture and they include: society, economy and ecology. Furthermore, the framework of sustainable architecture provides specific examples of building characteristics from each domain.

7. Instead of including one or a few building characteristics and simulating them to a high degree (low resolution and high depth of a building environment), it would be desirable to include more aspects and simulate them in less-details (high resolution and low depth of a building environment). Thus, specification of some complex building characteristics (such as for example energy consumption, ventilation or acoustic performance) should be implemented with an increasing level of accuracy, adjusted to the growing computational

capacity of machines. Alternatively, selected complex building characteristics could be omitted in the first implementations of the system and included later.

8. The building environment should offer a default parameter for each building characteristic it contains, in order not to necessitate a user to define a large number of building characteristic. The default set of parameters should be based on the framework of sustainable

architecture.

PRINCIPLES RELATED TO THE EVOLUTIONARY ALGORITHM 9. The evolutionary algorithm should apply mutation rate control. It is

more desirable to have a large mutation rate at the beginning of the generation process and then to focus on refining the solutions that have been found so far. Due to large mutation rates, a large part of the search space could be examined at the beginning of the search process, increasing probability of finding good solutions.

10. The algorithm should use parameter adjustment. The building model’s development could be improved by application of a dynamic adjustment of such parameters as mutation rate control or probability of mutation and crossover. Practically, these parameters would be the subject of the evolutionary process. For example the level of decomposition of a building model can be a parameter that is adjusted dynamically. The decomposition can be deeper at the beginning of the adaptation and decrease gradually towards the end of the process.

11. The evolutionary algorithm should apply a parallel-terraced scan.

This technique optimally allocates the available searching resources in order to find the best balance between exploration and

exploitation of a search space. Thus, at the initial phase of the search (generation) process, building models should be very different from each other, exploring a possibly large space of design versions.

Successively, the algorithm should focus on refining a few best-fitted building models. At the same time, the algorithm should allocate a fragment of the search resources on a random search for very different alternatives.

12. The algorithm should employ penalty functions – a method for eliminating deficient (for example spatially inconsistent) building models from evolving population.

13. The algorithm should use a tournament selection. It is a method for relative evaluation of building models. Instead of evaluating a building in absolute terms (for example by points) the algorithm

should indicate which model is ‘better’ in a direct comparison (‘tournament’) concerning selected building characteristic. For example the actual cost of a building does not have to be determined; instead, the algorithm can indicate which of two selected buildings would be more costly. Alternatively, the algorithm might use representational units for rating building models.

14. A user intervention in the process of building models evaluation should be allowed. The user should be able to monitor the adaptation process and intervene in defined periods to support the automatic evaluation of building models. This seems desirable because of the qualitative nature of some building characteristics (such as a building shape) which make automatic evaluation problematic.

15. Multimodal problems, multi-objective problems and the Pareto front are tactics from which the generative design system could probably benefit most. These tactics involve evolution that considers many different objectives (often opposing each other) at the same time.

Design problems are a class of multi-objective problems, because they involve several design objectives (the building environment is inclusive). There are two main approaches to multi-objective problems. In the first approach, a multi-objective problem can be

‘reduced’ to a single-objective problem by a system of ‘weights’.

The generative design system would produce a single, best-fitted building model. In the second approach, a simulation of

environmental niches can be applied, in which the building models evolve in parallel, adjusting to carefully selected subsets of design objectives. The generative design system would then offer a number of building models, which are best trade-offs between the subsets of design objectives (a Pareto front).

1.3. THE BENEFITS OF THE SYSTEM AND THE