growing trees with the genetic algorithm our goal
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Growing Trees with the Genetic Algorithm Our Goal Catch as much - PowerPoint PPT Presentation

Growing Trees with the Genetic Algorithm Our Goal Catch as much sun as possible! Implementation - Setup Unreal Engine 4 Ray traces Simple fitness function Building of graphical interface The Genetic Algorithm


  1. Growing Trees with the Genetic Algorithm

  2. Our Goal ● Catch as much sun as possible!

  3. Implementation - Setup ● Unreal Engine 4 ● Ray traces ● Simple fitness function ● Building of graphical interface

  4. The Genetic Algorithm ● General idea - Evolution ● Fitness ● Stochastic selection ● Combining DNA ● Incremental improvement ● Complexity vs Creativity ● Why is GA suitable for our problem?

  5. Implementation - Algorithm ● A functioning algorithm ● Physical and DNA representation of tree, branches, leafs ● Mutation ● Sexual vs Asexual reproduction ● Fitness functions ● Convergence ● Population

  6. Fitness Function ● A function that evaluates a tree for each generation tick ● Mimics the sun ● Different types ● Experimenting

  7. Fitness Function - Improvements ● Parallell rays ● Player controlled functions

  8. Fitness Function type - Normal (straight above)

  9. Fitness Function type - Manual ● Any direction

  10. Fitness Function type - Sweep ● Shoots rays from multiple angles ● Gave somewhat vague results

  11. Fitness Function type - Hemisphere ● Trail and error ● Gave good results with increased res.

  12. Fitness Straight Above Generation 1 Generation 10 000

  13. Hemisphere Fitness Generation 1 Generation 16 000

  14. Changing Environment ● User controlled obstacles ● Cubes ● Rocks ● Plates ● All scalable and rotatable

  15. Comparison - Fitness Straight Above

  16. Results ● With what can we compare our results?

  17. Implementation - Improving the algorithm ● Sexual reproduction ● Modular data structure for branches ● Soft random selection ● Replacements per generation ● Lower mutation frequency, more possible mutations

  18. Hill climbing ● Should only be performed when GA seems to have converged. ● Reaches local maximum. ● Destroys possibility to continue genetic algorithm.

  19. GUI

  20. Conclusion ● Problems, solutions, lessons learned ○ Selection ○ Reproduction ○ Data structure ○ Fitness ● Weaknesses and strengths of GA ○ Creativity ○ Complexity ○ Dependent on ad-hoc algorithms. ● Overall, satisfying results and our goals were reached.

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