1. Dynamic Layout Optimization for Newspaper Web Sites using a Controlled Annealed Genetic Algorithm Gjermund Brabrand H06MMT

2. Introduction Thesis Research questions Method Prototype Results Conclusions Further work Index

3. Introduction What is layout optimization? Finding the best layout for a given purpose What is the problem with most newspaper web site presentations today? Static layout Oversized Space efficiency How can newspaper web site presentations benefit dynamic layout?

4. Thesis A layout generator for newspapers Problems Control - interaction Individual apperance Supervision Workflow

5. Thesis A newspaper layout consists of rectangles laid out on a surface in a way that produce no gaps, and looks good. The pagination problem computerized process by which layout components is laid out Annealed genetic algorithm evolutionary algorithm used for search and optimization problems

6. Research questions RQ1: How can automated layout procedures benefit a newspaper web site advantageously? RQ2: How can article control be implemented in the algorithm fitness function without loss of effectiveness and performance? RQ3: How well does the fitness function and human eye correlate in picking out visually approved layouts?

7. Research questions RQ4: What positive and negative factors will automated layout in a newspaper web site have on the user workflow compared to regular news posting?

8. Method RQ1: A prototype is developed using standard CMS design with a layout generator implemented. A group of personel with relevant experience will compare regular news publishing layout with the protoype.

9. Method RQ2: The prototype is used to test out different solutions for article control. Test of performance and runtime will determine which solution to use. RQ3 is used to answer this questions visual performance issue.

10. Method RQ3: An experiment is carried out to check for correlation between human eye and the fitness function.

11. Method RQ4: Based on a survey answered by the prototype test participants we try to uncover significant changes in prototype workflow compared with regular publishing systems.

12. Prototype Principle of the prototype

13. Prototype Annealed genetic algorithm Initial solution (chromosome) chrom = [ 2 5 1 9 7 8 3 6 4 ] Mutation ”A bad solution is often close to a good solution” Prevent local optima chrom = [ 2 5 3 9 7 8 1 6 4 ] Control operators (discussed later) Calculate fitness Check solution If new.fitness < current.fitness  hold If new.fitness within acceptance domain  hold

14. Prototype Swap operator Alt 1 1.Initial solution Chrom = [ 2 5 1 9 7 8 3 6 4] 2.Mutation 3.Calculate fitness 4.Check for size match in better positions Typical result: Chrom = [ 2 3 4 9 7 8 5 6 1] Alt 2 1.Initial solution Chrom = [ 2 5 1 9 7 8 3 6 4 ] 2.Mutation 3.Put priority articles first Chrom = [ 7 3 4 9 2 8 5 6 1] 3. Calculate fitness

15. Prototype Headliner operator 1. Initial solution chrom = [ 2 5 1 9 7 8 3 6 4 ] 2. Mutation 2. Fixed solution chrom = [ 3 5 1 9 7 8 2 6 4 ] 3. Calculate fitness

16. Results RQ1: Prototype Dynamic without being accidental Autogenerated category sites Choose layout profile Article control Experiment Group 1: test of prototype - survey Group 2: fitness functino vs. human eye Performance

17. Results RQ2: Algorithm performance is maintained RQ3: Correlation between fitness function and human eye 10 participants vs. 8 random fitness solutions

18. Results Survey 6 participants Experience with web publishing systems (CMS) Work at large newspaper web sites RQ4: Outcome Not enough positioning control of individual articles ”think design” Easy to use

19. Conclusion Not enough control for professional use Frequent change of layout Positioning control Usefull in other areas Personalized presentations Webshop product presentations Smaller newspapers/online publications Choice of method

20. Further work Test in other areas Linked articles Expand function gallery Advertisement support