11378 Special Project (Advanced Computational Design)
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Subject handbook information prior to 2023 is available in the Archives.
Credit points: 6 cp
Result type: Grade and marks
Requisite(s): (11368 Special Project (Computational Design) AND (48 credit points of completed study in spk(s): C10271 Bachelor of Design Interior Architecture OR 48 credit points of completed study in spk(s): C10272 Bachelor of Design Interior Architecture Bachelor of International Studies OR 48 credit points of completed study in spk(s): C10322 Bachelor of Design Interior Architecture Bachelor of Creative Intelligence and Innovation OR 48 credit points of completed study in spk(s): C10423 Bachelor of Design Interior Architecture Bachelor of Languages and Cultures OR 72 credit points of completed study in spk(s): C10004 Bachelor of Design Architecture OR 72 credit points of completed study in spk(s): C10413 Bachelor of Design Architecture Master of Architecture OR 72 credit points of completed study in spk(s): C10325 Bachelor of Design Architecture Bachelor of Creative Intelligence and Innovation OR 48 credit points of completed study in spk(s): C09079 Bachelor of Landscape Architecture (Honours)))
These requisites may not apply to students in certain courses. See access conditions.
Description
Elective 11378 introduces students to the wonderful world of multi-objective evolutionary computation. Through the use of advanced computation and parametric tools, the elective examines how we as designers deal with complex design problems comprised from multiple conflicting objectives. Students learn the necessity of quantifying design goals and variables to the parametric process, and how big data can be leveraged to make informed decisions at all stages of the design process.
The role of the evolutionary process, and its inherent biological principals for the adaptation and survival of species, exemplifies its significance in shaping the natural world. No other process has exhibited such efficiency in generating morphological variation of form that is adapted to specific external conditions. As such, Evolutionary Algorithms (EAs) have been used extensively, across a multitude of disciplines, to apply biological principles towards solving common real-world problems that are too complex to solve through conventional means. In design, the integration of these algorithms within some of the most widely used computational modelling software has made designers’ ability to apply evolutionary processes as design models more streamlined than in any other point in history. In addition to this, the role that data analytics plays in the design process is vital to ensure a better understanding of the design problem as well as the results generated by its formulation. In doing so, architectural, environmental, climatic, and structural analytics form the framework to the efficient and successful formulation of any design problem – where these analyses are part of the conceptual development of the design and not dedicated to only understanding the end product.
The elective uses these advanced computational design methods derived from biological evolutionary principles to better understand, and be more critical, of how evolutionary principles can benefit the design process, and what role numeric data plays in their application. Students learn the fundamentals of evolutionary algorithms and the various processess associated with developing a working parametric model for running an evolutionary algorithm, as well as analysing the results outputted by the algorithm. The elective uses Grasshopper 3D and Wallacei as the primary tools for running the parametric models.
Subject learning objectives (SLOs)
On successful completion of this subject, students should be able to:
| 1. | Gain expertise in Parametric Modelling using Grasshopper 3D |
|---|---|
| 2. | Demonstrated ability to run an evolutionary simulation on simple and complex problems as well as read and express the data outputted by the evolutionary simulation through multiple analytic methods |
| 3. | Demonstrated ability to utilise advanced digital tools and various scales, examining their application on global and local architectural scales, and resolving the design challenges associated with both these scales |
| 4. | Demonstrated ability to visualise parameter based outputs through various mediums, including animations |
Course intended learning outcomes (CILOs)
This subject also contributes to the following Course Intended Learning Outcomes:
- Creatively use architectural media, technologies and materials (I.2)
- Understand and challenge disciplinary conventions through an engagement with emergent forms of architectural practice, technologies and modes of production (P.1)
- Independently analyse, synthesise and formulate complex ideas, arguments and rationales and use initiative to explore alternatives (R.3)
Teaching and learning strategies
The class is a highly technical subject. Students are expected to follow along the in-class exercises, as well as the out of class exercises. A significant aspect of learning parametric design tools is the time needed practicing the knowledge gained outside of class time. It is imperative that students complete all out of class exercises and arrive to each class prepared for new material. The subject will assess student knowledge using various mediums, including exams and technical applications in their studio work.
Content (topics)
The subject will explore the applications of evolutionary computation in design. The main topics the subject will investigate are:
- Bootcamp - Parametric modelling in Grasshopper
- Evolutionary computation in Architecture
- Formulating the Design Problem in Grasshopper
- Calculating Fitness Functions for Optimisation
- Running a Evolutionary Algorithm and analysing the results
- Exporting Solutions from the Algorithm
- Creating Animations
- Wallacei Analytics
- Decoding the Architectural Genome
Assessment
Assessment task 1: AT1
| Intent: | The exams in the first three assessment tasks will test the student’s expertise and knowledge on various theoretical and grasshopper methods, techniques, and skills associated with parametric modelling and Evolutionary computation. The exam will be held in class time and will be multiple choice. | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 1, 2 and 3 This task also addresses the following course intended learning outcomes that are linked with a code to indicate one of the five CAPRI graduate attribute categories (e.g. C.1, A.3, P.4, etc.): I.2, P.1 and R.3 | ||||||||||||||||
| Type: | Examination | ||||||||||||||||
| Groupwork: | Individual | ||||||||||||||||
| Weight: | 50% | ||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Assessment task 2: AT2
| Intent: | The exams in the first three assessment tasks will test the student’s expertise and knowledge on various theoretical and grasshopper methods, techniques, and skills associated with parametric modelling and Evolutionary computation. The exam will be held in class time and will be multiple choice. | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 1, 2 and 3 This task also addresses the following course intended learning outcomes that are linked with a code to indicate one of the five CAPRI graduate attribute categories (e.g. C.1, A.3, P.4, etc.): I.2, P.1 and R.3 | ||||||||||||||||
| Type: | Examination | ||||||||||||||||
| Groupwork: | Individual | ||||||||||||||||
| Weight: | 25% | ||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Assessment task 3: AT3
| Intent: | The final assessment will require students to submit an animation created in Grasshopper for their studio project. The project itself does not have to have been modelled in grasshopper, however the animation must be through grasshopper. Students are required to also submit the grasshopper definition and rhino file used to create the animation. Student are assessed on the complexity of the animation and role the animation plays in better describing the project being animated. | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Objective(s): | This task addresses the following subject learning objectives: 1, 2 and 3 This task also addresses the following course intended learning outcomes that are linked with a code to indicate one of the five CAPRI graduate attribute categories (e.g. C.1, A.3, P.4, etc.): I.2, P.1 and R.3 | ||||||||||||||||
| Type: | Project | ||||||||||||||||
| Groupwork: | Individual | ||||||||||||||||
| Weight: | 25% | ||||||||||||||||
| Criteria linkages: |
SLOs: subject learning objectives CILOs: course intended learning outcomes |
Minimum requirements
The DAB attendance policy requires students to attend no less than 80% of formal teaching sessions (lectures and tutorials) for each class they are enrolled in to remain eligible for assessment.
Recommended texts
- Decoding the Architectural Genome: Multi-Objective Evolutionary Algorithms // Mohammed Makki, Diego Navarro-Mateau, Milad Showkatbakhsh
- Evolutionary Multi-Objective Optimization: A Historical View of the Field // Carlos A. Coello Coello
- An Overview of Genetic Algorithms: Part 1, Fundamentals // David Beasley, David Bull, Ralph Martin
- Foundations of Evolutionary Computation // David Fogel
- Evolutionary Computation: A Unified Approach (Chapters 1 and 2) // Kenneth De Jong
- Addressing Flood Resilience in Jakarta’s Kampungs Through the use of Sequential Evolutionary Simulations // Kim Ricafort, Ethan Koch, Mohammed Makki
- The design of social and cultural orientated urban tissues through evolutionary processes // Jason Choi, Christina Nguyen, Mohammed Makki