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ShapeGenetics: Using Genetic Algorithms for Procedural Modeling
Author(s) -
Haubenwallner Karl,
Seidel HansPeter,
Steinberger Markus
Publication year - 2017
Publication title -
computer graphics forum
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.578
H-Index - 120
eISSN - 1467-8659
pISSN - 0167-7055
DOI - 10.1111/cgf.13120
Subject(s) - reversible jump markov chain monte carlo , procedural modeling , computer science , skyline , algorithm , convergence (economics) , set (abstract data type) , silhouette , tree (set theory) , population , markov chain , genetic algorithm , monte carlo method , representation (politics) , mathematical optimization , theoretical computer science , markov chain monte carlo , artificial intelligence , machine learning , data mining , mathematics , bayesian probability , programming language , mathematical analysis , statistics , demography , sociology , economics , economic growth , politics , political science , law
In this paper, we show that genetic algorithms (GA) can be used to control the output of procedural modeling algorithms. We propose an efficient way to encode the choices that have to be made during a procedural generation as a hierarchical genome representation. In combination with mutation and reproduction operations specifically designed for controlled procedural modeling, our GA can evolve a population of individual models close to any high‐level goal. Possible scenarios include a volume that should be filled by a procedurally grown tree or a painted silhouette that should be followed by the skyline of a procedurally generated city. These goals are easy to set up for an artist compared to the tens of thousands of variables that describe the generated model and are chosen by the GA. Previous approaches for controlled procedural modeling either use Reversible Jump Markov Chain Monte Carlo (RJMCMC) or Stochastically‐Ordered Sequential Monte Carlo (SOSMC) as workhorse for the optimization. While RJMCMC converges slowly, requiring multiple hours for the optimization of larger models, it produces high quality models. SOSMC shows faster convergence under tight time constraints for many models, but can get stuck due to choices made in the early stages of optimization. Our GA shows faster convergence than SOSMC and generates better models than RJMCMC in the long run.