OK, this is going to get a bit deep but it is necessary to understand how PoseGenie does what it does. A genetic algorithm is a method of arriving at a good solution for a problem that has a very large number of possible combinations. As an example, take the "nose touch" problem in the first image. There are a very large number of possible joint rotations involved in getting the Poser figure to touch the tip of a finger to it's nose. If the pose only uses the collar, shoulder, and forearm and you limit the rotations to whole numbers for -360 to 360 you get: 720 (integer degrees from -360 to 360) times 3 (x, y and z rotations) times 3 (number of body parts) = 6,480 combinations Only a very few of those 6,480 combinations are going to actually get the finger close to the nose. An even smaller number will look good since most will contort the limbs into un-natural configurations. But in fact, the number of possible combinations is much larger since Poser doesn't use whole number increments in the rotations. With just three places after the decimal you've now got: 6,480 times 1,000 = 6,480,000 possible combinations Granted, in most instances you wouldn't have a situation here all the joints could rotate from -360 to 360 but regardless you'd still end up with a very large number of possible combinations. Finding possible solutions when dealing with a very large number of combinations can be a daunting task. A genetic algorithm is useful in situtions like this because it can sift through the possible combinations and arrive a good solution (though not always the best solution). It does this by simulating the processes that living organisms use to pass on genetic traits. (more)