genetic implementation

Hi to all,

if I have a lot of rules like this:

object(name, strength, list of answers).

EXAMPLE:
object(apple, 100, [s(1, y), s(2, n)]).
object(apple, 150, [s(1, n), s(2, y)]).
object(apple, 200, [s(2, n), s(3, n)]).
object(apple, 250, [s(2, n), s(4, y)]).

and I want to make a genetic algorithm that produce new rules I have thought these steps:

- take all the rules with Strength >=X (X is fixed by me, for example 100)
- choose at random (rules with more strength have more probability to be choosen) a couple of rules that will produce two new rules
- choose a K that means that K answers will be exchanged between two rules
- make the mutation in a very small case in


is this a correct implementation in my case?

Thanx
Marco
.

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