# RolfAndreassen comments on Seeking advice on using evolutionary methods to solve the 3-body problem - Less Wrong Discussion

-4 08 August 2012 11:43PM

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Comment author: 09 August 2012 03:17:53AM *  13 points [-]

Edit to add: In the spirit of being a bit more helpful, I believe you are being downvoted because your question indicates that you don't really understand evolutionary algorithms or what a solution to the three-body problem would consist of. It appears that you have tried the algorithm of "random new-ish method, random famous old problem, apply one to the other" without actually knowing anything about either of the two random selections. Come back when you know enough about the problem to state why you picked evolutionary algorithms over neural networks, or Monte Carlo, or gods-help-us-all maximum-likelihood fitting.

Can I create an evolutionary derived algorithm to solve equations of motion?

I don't know; can you? The rest of your question seems a bit confused. An approximate, numerical solution an a closed-form solution are not the same kind of thing; there's no evolutionary path between them. You can have an algorithm that spits out predictions for the future positions and velocities of the three bodies, and is evaluated on their accuracy precalculated by another method; or you can have an algorithm that spits out equations that may or may not be closed-form solutions, and are evaluated on the accuracy of those equations' predictions for the future positions and velocities. There is an additional level of evaluation here which you seem to have missed, which honestly does not give me great hope for your solving the problem.

point me in the right direction.

Certainly. In order of decreasing feasibility:

1. Learn the difference between its and it's.
2. Understand the difference of levels I alluded to above.
3. Decide on your criterion of fitness for a solution.
4. Write a program for generating, 'breeding', 'mutating', evaluating, and culling algorithms.
5. Decide which approach you intend to pursue, observing that we have perfectly satisfactory numerical methods anyway.
6. Find an algorithm for generating either numerical solutions or closed-form equations; the algorithm should have several tunable parameters and a smooth parameter space with, preferably, no local maxima.
7. With the insights generated by the previous step, realise that you don't actually need the evolutionary approach.
8. Write the PhD thesis.
Comment author: 09 August 2012 03:15:14PM 0 points [-]

random new-ish method, random famous old problem, apply one to the other

For what it's worth, that's a tried-and-true method to be extremely successful. Sadly, "machine learning" is the new electric motor, not "evolutionary algorithms".