SYSTEMS AND METHODS FOR AUTOMATED DESIGN

1 . A design optimization method comprising:

preparing a symbolic tree;

updating node symbol parameters using a plurality of samples;

sampling the plurality of samples with a method for solving a multi-armed bandit problem;

promoting each sample in the plurality of samples down a path of the symbolic tree;

evaluating each path with a fitness function; and

outputting a path of the symbolic tree.

2 . The design optimization method of claim 1 further comprising:

providing at least one design parameter.

3 . The design optimization method of claim 2 wherein the at least one design parameter comprises one of:

a discrete parameter; and

a continuous parameter.

4 . The design optimization method of claim 1 further comprising:

providing a plurality of design parameters, the plurality of design parameters further comprising: discrete parameters and continuous parameters.

5 . The design optimization method of claim 1 wherein the method for solving the multi-armed bandit problem comprises Thompson sampling.

6 . The design optimization method of claim 5 further comprising:

sampling using batch;

computing a success rate; and

updating Thompson parameters.

7 . The design optimization method of claim 1 further comprising:

providing an error function, the error function defining a design objective.

8 . The design optimization method of claim 7 , wherein the design objective comprises an optical system design objective.

9 . A computer implemented optimization method comprising:

initializing a symbolic tree in a preparation phase;

updating parameters held by each node in the symbolic tree using samples collected during an epoch in a parameter phase;

evaluating at least one sample down the symbolic tree with Thompson sampling in order to select at least one sample in a Thompson phase; and

updating parameter distributions using the selected at least one sample and incrementing the epoch in a rejection phase.

10 . The computer implemented optimization method of claim 9 wherein the preparation phase further comprises:

generating a tree node with two sets of distributions, wherein each tree node contains a Thompson Distribution.

11 . The computer implemented optimization method of claim 9 wherein each node contains a plurality of parameter priors for each of its respective parameters.

12 . The computer implemented optimization method of claim 9 wherein the parameter phase further comprises:

determining a batch size and an error value for the epoch.

13 . The computer implemented optimization method of claim 12 wherein the parameter phase further comprises:

setting a batch size to be a number of samples taken in each rejection phase.

14 . The computer implemented optimization method of claim 9 wherein the parameter phase further comprises:

updating parameter distributions using saved samples and incrementing the epoch.

15 . The computer implemented optimization method of claim 9 wherein the rejection phase further comprises:

evaluating an error function for a selected path on the symbolic tree.

16 . The computer implemented optimization method of claim 15 wherein the error function defines a design objective.

17 . An optimization system comprising:

a computer system, the computer system further comprising:

at least one processor;

a graphical user interface; and

a computer-usable medium embodying computer program code, the computer-usable medium capable of communicating with the at least one processor, the computer program code comprising instructions executable by the at least one processor and configured for:

preparing a symbolic tree;

updating node symbol parameters using a plurality of samples;

sampling the plurality of samples with a method for solving a multi-armed bandit problem;

promoting each sample in the plurality of samples down a path of the symbolic tree;

evaluating each path with a fitness function; and

outputting a path of the symbolic tree.

18 . The optimization system of claim 17 further comprising:

providing at least one design parameter, the at least one design parameter comprising one of:

a discrete parameter; and

a continuous parameter.

19 . The optimization system of claim 17 wherein the method for solving the multi-armed bandit problem comprises Thompson sampling further comprising sampling using batch; computing a success rate; and updating Thompson parameters.

20 . The design optimization system of claim 17 further comprising:

providing an error function, the error function defining a design objective.