Wei, the result of my own program makes no sense to me. It wasn’t predicted by any of our prior arguments. MacKay says that the supportable information should go as the square root of the genome size, not that supportable information should go as the inverse square of the mutation rate. We’re not getting a result that fits even with what MacKay said, let alone with what Williams said; and, I should point out, we’re also not getting a result that fits with there being <25,000 protein-coding regions in the human genome.
Maybe you can’t sort and truncate the population and have to use probabilistic reproduction proportional to fitness? If so, that would indicate the intuitive argument from “4 children, 2 survivors” is wrong. But in my own experiments fitness did seem roughly proportional to log children, and not proportional to (square root) genome size, which was the only part I had thought to predict.
The big puzzle here is the inverse square of the mutation rate. The example of improvement in a starting population with a randomized genome of maximum variance, which can’t be used to send a strongly informative message, doesn’t explain the maintenance of nearly all information in a genome.
Are there any professional evolutionary theorists in the audience? Help!
Wei, the result of my own program makes no sense to me. It wasn’t predicted by any of our prior arguments. MacKay says that the supportable information should go as the square root of the genome size, not that supportable information should go as the inverse square of the mutation rate. We’re not getting a result that fits even with what MacKay said, let alone with what Williams said; and, I should point out, we’re also not getting a result that fits with there being <25,000 protein-coding regions in the human genome.
Maybe you can’t sort and truncate the population and have to use probabilistic reproduction proportional to fitness? If so, that would indicate the intuitive argument from “4 children, 2 survivors” is wrong. But in my own experiments fitness did seem roughly proportional to log children, and not proportional to (square root) genome size, which was the only part I had thought to predict.
The big puzzle here is the inverse square of the mutation rate. The example of improvement in a starting population with a randomized genome of maximum variance, which can’t be used to send a strongly informative message, doesn’t explain the maintenance of nearly all information in a genome.
Are there any professional evolutionary theorists in the audience? Help!