4 ms·

It is not quite a genetic algorithm: there is no population of individuals s which get mated and possibly mutated. It's more of a dynamic programming polygon m

• michaels0620 14y ago
  He answers this in his faq (http://rogeralsing.com/2008/12/09/genetic-programming-mona-lisa-faq/ http://rogeralsing.com/2008/12/09/genetic-programming-mona-l...): Q) Is this Genetic Programming?, I think it is a GA or even a hill climbing algorithm. A) I will claim that this is a GP due to the fact that the application clones and mutates an executable Abstract Syntax Tree (AST). Even if the population is small, there is still competition between the parent and the child, the best fit of the two will survive.
  • ufo 14y ago
    No matter how he tries to twist things, its still just regular hill climbing though. The whole point of GAs is giving you a very simple heuristic for avoid local optima. How are you going to do that if your population size is only 2?
• cmsmith 14y ago
  Exactly, the 'genetic' part of the term implies some sort of breeding, not that you can transform your search space into a vector which you call a genome. In fact, the algorithm used seems to be exactly what is described on the wikipedia for 'Random Optimization' [1]. I would expect that a true GA might work better, but not be the best choice. In my semi-related experience, Particle Swarm Optimization [2] works much better for continuous valued problems. [1] http://en.wikipedia.org/wiki/Random_optimization http://en.wikipedia.org/wiki/Random_optimization [2] http://en.wikipedia.org/wiki/Particle_swarm_optimization http://en.wikipedia.org/wiki/Particle_swarm_optimization