Dynamic Layout Optimization for Newspaper Web Sites using a Controlled Annealed

Genetic Algorithm

Gjermund BrabrandH06MMT

• Introduction• Thesis• Research questions• Method• Prototype• Results• Conclusions• Further work

Index

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?

Thesis

• A layout generator for newspapers• Problems

• Control - interaction• Individual apperance• Supervision• Workflow

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

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?

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?

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.

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.

Method

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

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.

Prototype

• Principle of the prototype

PrototypeAnnealed 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

Prototype Swap operator

• Alt 1

1. Initial solutionChrom = [ 2 5 1 9 7 8 3 6 4]

2. Mutation3. Calculate fitness4. Check for size match in better

positionsTypical result:Chrom = [ 2 3 4 9 7 8 5 6 1]

• Alt 2

1. Initial solutionChrom = [ 2 5 1 9 7 8 3 6 4 ]

2. Mutation3. Put priority articles first

Chrom = [ 7 3 4 9 2 8 5 6 1]3. Calculate fitness

PrototypeHeadliner operator

1. Initial solutionchrom = [ 2 5 1 9 7 8 3 6 4 ]

2. Mutation2. Fixed solution

chrom = [ 3 5 1 9 7 8 2 6 4 ]3. Calculate fitness

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

Results• RQ2: Algorithm performance is maintained

• RQ3: Correlation between fitness function and human eye

10 participants vs. 8 random fitness solutions

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

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

Further work

• Test in other areas• Linked articles• Expand function gallery• Advertisement support