Matt Gingel noted briefly:
> In the general case, evolution is a graph search. Each possible
> individual is a node, and mutations spell out the transition rules. By
> manipulating the space of transition rules, we transform the search
> space - we make some points closer together and some further
> apart. Perhaps more importantly, local maxima can be eradicated
> since we change the set reachable nodes. (This analysis still
> holds if we consider crossover operators, every node becoming a
Here's a good example of how someone might use GAs: they might be
interested in finding out what the most aerodynamic design for a
vehicle is. So they'd design a multi-dimensional space allowing for a
variety of different things to be changed in the design of the craft.
Then, they'd set the GA to work.
When it got to work, it would generate a random sampling of designs,
CHECK TO SEE HOW AERODYNAMIC THEY WERE, and prioritize the designs
which were the most aerodynamic in the next run, which would amount to
random variations, breedings, etc. from the previous generation.
So. GA. Trying to generate good heuristics. It generates a random
sampling of heuristics (or sets of heuristics). Then it checks to see
which heuristics are best, and prioritizes the best heuristics in the
But how do you figure out whether a heuristic is good or not? Well,
heuristics are rules for action. You USE the heuristic, and you see
how well off you wind up. (Using some definition of "well off" which
has nothing to do with motivations, I suppose.) In fact, just one
trial run really isn't enough... you need to try using the heuristic
many many times in order to even out statistical hiccups.
So, how many operations do you think it would take to reach a decision
on the heuristic "Honesty is the best policy?" Would 10^21 be enough?
Or would it take time to do the testing phase?
This is like a "good coffee" maker. How do you identify good
-unless you love someone-
-nothing else makes any sense-
This archive was generated by hypermail 2b29 : Thu Jul 27 2000 - 14:11:32 MDT