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Right, I was thinking it was more of a random walk algorithm: http://en.wikipedia.org/wiki/Random_walk http://en.wikipedia.org/wiki/Random_walk There was too m
by maercsrats 15y ago
Right, I was thinking it was more of a random walk algorithm: http://en.wikipedia.org/wiki/Random_walk http://en.wikipedia.org/wiki/Random_walk
There was too much randomness in his evolving. When I was evolving agents for my M.S., I used tournament selection (http://en.wikipedia.org/wiki/Tournament_selection http://en.wikipedia.org/wiki/Tournament_selection) with an 80% crossover rate for the top 2 winners and only a 5% mutation rate on the offspring. I also started off with a population of 500 individuals. This approach seemed to produce much better results.
- user24 15y agoYeah, I was taught roulette wheel at university but I think that tournament is a much better way of doing selection.
- bermanoid 15y agoThere was too much randomness in his evolving. When I was evolving agents for my M.S., I used tournament selection (http://en.wikipedia.org/wiki/Tournament_selection http://en.wikipedia.org/wiki/Tournament_selection) with an 80% crossover rate for the top 2 winners and only a 5% mutation rate on the offspring. I also started off with a population of 500 individuals. This approach seemed to produce much better results. I don't know about that - with a population size of 500, every generation is very expensive to test, so your runway is a lot shorter. I set up a test using your parameters, and it seemed to take an average of about 50 generations to evolve "Hello, World". That's a total of 50x500=25000 fitness evaluations (assuming you've cached results properly) to solve the problem. Whereas with a population size of 20, 100% crossover and mutation rates, the average is about 150 gens, for 150x20 = 3000 evaluations, beating your parameter set by a factor of 8. Which doesn't mean much, other than that this is not such a good problem to use evolutionary methods on.