The slides for the PACMHCI / EICS 2021 paper titled "Grid-based Genetic Operators for Graphical Layout Generation" authored by Morteza Shiripour, Niraj Ramesh Dayama, Antti Oulasvirta For more Details: https://doi.org/10.1145/3461730

1. Grid-based Genetic Operators
for Graphical Layout Generation
Morteza Shiripour, Niraj Ramesh Dayama, Antti Oulasvirta
Aalto University, Finland
EICS 2021

2. Layout design
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3. Many-objective optimization problem
𝑀 is the number of objectives
𝑥 is the feasible set of decision variables
Ω is the set of all boundaries and constraints
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4. How to solve this problem?
● Classical approaches:
○ Weighted-sum and 𝜖-constraint
i. Multiple runs are required
ii. Obtaining the full Pareto front is difficult
iii. Sensitive to the Pareto front’s shape
iv. Linear approximation techniques for some objectives
● Evolutionary approaches:
○ Non-dominated sorting genetic algorithm (NSGA) III
i. Easy to implement
ii. A broad applicability
iii. Robust to dynamic changes
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5. The proposed
NSGA-III flowchart
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6. Chromosome representation
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7. Initial generation
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8. Original uniform-crossover operator
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9. Grid-based crossover operator
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10. Grid-based
crossover steps
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11. Performance comparison
Inverted generational distance (IGD)
is the minimum distance from
Pareto reference 𝑖 to Pareto solution 𝑞
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12. Performance comparison
Hypervolume (HV)
𝑆 is an index for the Pareto approximation solutions
𝑅 is a reference point
is the volume of solution 𝑖
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13. Pareto presentation
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14. Perceived quality by users
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15. Conclusion
● All the solutions generated satisfy the no-overlap requirement
● Representing well-formed layouts
● The efficiency of the generated layouts was verified by users
Future work
● Exploring new objectives and their effects on performance
● Emerging work that combines machine learning approaches
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16. Thank you!
Funding:
Technology Industries of Finland
Academy of Finland
Email:
shiripour.morteza@aalto.fi