4 ms·
One way you can use a higher mutation rate is to only mutate children, but keep the parents without mutation (as the first two children of the next generation).
by frikk 11y ago
One way you can use a higher mutation rate is to only mutate children, but keep the parents without mutation (as the first two children of the next generation). That way you can always remember the 'best of previous generations.
Or, you can vary the mutation rate with a normal curve -- so say most of the population gets a small rate, but a small percentage gets a wildly higher rate and wildy lower rate.
- dragonbonheur 11y ago>remember the 'best of previous generations In nature, immortality doesn't seem to lead to much evolution. remember that immortal jellyfish some japanese scientist discovered a while ago that went back to its polyp phase once it got mature? Besides, sometimes species devolve too in nature. I just accept mortality and obsolescence as they are.
- frikk 11y agohehe. That's fine. It all depends on the nature of the experiment. Typically you want to optimize a solution in a given deterministic domain. If you wish to, instead, create an open-ended evolutionary system for novelty in a non-deterministic solution space, go for it! Although, to be fair, in nature you're also not dealing with a static fitness function, a high mutation rate, and there's a wide myriad of other variables that affect the overall dynamics of a population (like sexual vs asexual reproduction, migration, recessive genes, sexual selection, etc)
- dragonbonheur 11y agoYou're absolutely right. I've been wondering what results i would get if I seeded all of the population with the reference image data, slightly mutated but let the GA look for variations with a 10 or 20% variation tolerance.