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    SubjectRe: [ANNOUNCE 0/4][RFC] Genetic Algorithm Library
    Do you have any crossover?  This is critical for GA to work well - 
    without it, the algorithm is really only a parallel random search. More
    specifically, is step 6 pure copies of a single parents, or can children
    inherit tunables from multiple parents?
    - Jim

    Jake Moilanen wrote:
    > ...
    > The basic flow of the genetic algorithm is as follows:
    > 1.) Start w/ a broad list of initial tunable values (each set of
    > tunables is called a child)
    > 2.) Let each child run for a timeslice.
    > 3.) Once the timeslice is up, calculate the fitness of the child (how
    > well performed).
    > 4.) Run the next child in the list.
    > 5.) Once all the children have run, compare the fitnesses of each child
    > and throw away the bottom-half performers.
    > 6.) Create new children to take the place of the bottom-half performers
    > using the tunables from the top-half performers.
    > 7.) Mutate a set number of children to keep variance.
    > 8.) Goto step 2.
    > Over time the tunables should converge toward the optimal settings for
    > that workload. If the workload changes, the tunables should converge to
    > the new optimal settings (this is part of the reason for mutation).
    > This algorithm is used extensively in AI.
    > ...
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