Rehashing Design through Evolutionary Computation
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Rehashing Design through Evolutionary Computation
- 1. Rehashing Design through Evolutionary Computation LearnXDesign 2019 Paper 012 Miguel MONTIEL Ricardo SOSA
- 2. Meaning, aesthetics, historical relevance, etc. Function/Shape optimization Evolutionary Computational Systems Approach
- 4. Learning design history through GAs Evolve a population of randomly generated structures into designs for office chairs that match the stylistic parameters of the 1950s
- 5. Evaluating active-learning activities with GAs GA solutions Real-life solutions
- 6. Learning design theory through GAs
- 7. Guidelines for learning activities with GAs: • Select artefacts composed of few components; • Component arrangement; • GAs as part of larger learning experiences; • Align the formulation of GAs with studio projects; • Recreate design theory through GAs
- 8. Evolutionary design in the curriculum: • Evolutionary operators • Programming skills • GA modelling • Design GAs
- 20. Summary • Enhance design learning via computational thinking; • Active-learning activities with GAs; • Teaching evolutionary computation in “designerly” ways
- 21. Code informs design Design informs code
- 22. Teşekkürler Thank you Gracias Paper 012 Miguel Montiel miguel.montiel@aut.ac.nz Ricardo Sosa ricardo.sosa@aut.ac.nz
- 23. Expected learning outcomes: • Stylistic parameters; • Material selection; • Manufacturing technologies; • Etc. Complementary activities: • Group discussions; • Poster making; • Etc.
Notas del editor
- Evolutionary computational systems can enhance students’ understanding on how design solutions can be optimised (Ercan & Elias-Ozkan, 2015). However, it is still unclear how evolutionary computational systems can improve the understanding of qualitative aspects of designed artefacts such as meaning, historical relevance, etc.
- This paper examines how the formulation and implementation of one type of evolutionary computational systems, namely, genetic algorithms (GAs), may foster the learning of design history and theory. A genetic algorithm (GA) is an evolutionary computation technique where candidate solutions for a given problem are stochastically combined and evaluated in successive generations. In its most basic form, GAs are composed by an initial population of candidate solutions, a selection operator, a crossover operator and a mutation operator.
- The formulation and implementation of GAs can be aligned with design history learning in different ways. For instance, educators can organize design activities in which GAs are implemented so as to produce design solutions that reflect the stylistic parameters of specific historical periods. Students can refer to specialised documents when formulating and implementing these GAs.
- Educators can evaluate this type of activities in different way. One alternative is to look at the similarity between the topology of the solutions produced by the GAs and that of real-life examples.
- Learning opportunities with GAs are not limited to computational implementation. Analogic work with components of GAs may enhance students’ comprehension of theoretical concepts. Candidate solutions in GAs are usually represented as strings of information. These strings can be referred as genomes. Genomes are composed of N number of locus each of which contains a design parameter. Design educators can select an everyday artefact (e.g. a chair) and ask students to represent such artefact as a genome. Once the representation is completed, students can progressively modify the values of the locus that regulate specific physical features. This exploration may gradually lead to a different type of design solution.
- Guidelines to design active-learning activities with GAs would include: - Design activities based on artefacts composed of few components that are connected in standardized ways. - Design activities based on artefacts whose components can be organized in different ways to produce the same or almost the same behaviour. - Design activities with GAs as part of larger learning experiences that include other approaches to learning (i.e. verbal, auditory, etc.). - Consider how the logic to formulate selection, crossover and mutation operators may be linked with design practice and align the stages to formulate and implement GAs with the activities scheduled in studio-based courses. - Consider how the assessment of the solutions produced by a GA can be used to revisit specific theoretical aspects of design.
- For activities as the ones we have reviewed to take place effectively, students need to: Have an understanding of what evolutionary systems are; Be familiar with the components of Gas; Link their understanding of design with the implementation of GAs Develop programming skills
- These learning outcomes can be achieved via strategies that are compatible with how designers learn. For instance, educators can ask students represent GAs diagrammatically.
- Complementarily, educators can ask students to build cardboard versions of the selection, crossover and mutation operators.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- These type or active-learning experience may facilitate for students to understand theoretical design concepts such as those proposed by Wagner. To maximise learning analogic work with GAs can be complemented with activities such as group discussions, poster making, etc.
- Discussion on other ways in which design can inform programming activities and vice versa.
- Besides allowing students to get familiar with stylistic parameters of different epochs, active-learning activities with GAs can increase students’ awareness of other historical aspects of design (e.g. the role of materials and manufacturing technologies). To maximise learning of design history, work with GAs can be complemented with activities such as group discussions, poster making, etc.