So I'm reading the genetic algorithms
book I received
, An Introduction To Genetic Algorithms
Mitchell and it's quite interesting. I've known the basics of genetic
algorithms but I've never really programmed any. Generally, they work like
natual selection: you have a population of possible answers (e.g., a list of
cities to visit in a particular order) to a problem (e.g., the Traveling
Salesman, where you want to minimize the distance traveled) and you cross
breed the current population (by taking two (or more perhaps) answers,
selecting a portion of their results, and mixing the two to form one (or
more) new answers), and possibly mutating the result a bit (e.g., swapping
two random cities in a given answer). You keep doing this over and over
until you get an answer, or an answer that is good enough. You don't just
select potential answers to breed by random—no, you select them based upon
a certain criteria (in this case, those in the population with the shortest
answers so far) that drives the results towards your eventual goal.
This can't be applied to every problem though, and for those that it can be,
the trick is to pick a representation of the data that can be mixed up and
As I'm reading, I'm also doing certain exercises from the book to help
cement my understanding of the processes involved in programming genetic
algorithms. It's an enjoyable way to spend a few hours.
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