Multi-Generation Gaussian Process Optimizer

This is the Matlab version of the MG-GPO algorithm. Currently MG-GPO has only been implemented in Matlab.

If you'd like to use it in your research or project, please cite the following paper:

• Multi-objective multi-generation Gaussian process optimizer for design optimization

  Huang, Xiaobiao, Minghao Song, and Zhe Zhang. "Multi-objective multi-generation Gaussian process optimizer for design optimization." arXiv preprint arXiv:1907.00250 (2019).

Prerequisites

The following setup is not necessary but highly recommended if you want to archive a similar performance as shown in the MG-GPO paper.

Download and install the GPML Matlab code

To download and install the GPML Matlab code, please follow the official instructions here.

You can change the mean function, covariance function and Gaussian likelihood in the GPML/GPML_predict.m script as you need. More options can be found in the manual of GPML Matlab code.

Usage

Test run

1. Execute the run.m script within Matlab

The test run will optimize the 30D ZDT2 problem with MG-GPO + local GP. The test run should be done in 1 min.

Optimize your own objective function

To optimize your own objective function, you'll have to implement the function in Matlab as a .m script, and put it into the evaluators folder.

The signature of the function should be like:

function obj = YOUR_BADASS_PROBLEM(x)

Please refer to the demo objective functions Rosenbrock (single objective) and ZDT2 (multi-objective) that located in the evaluators folder to get more details.

After completing your objective function setup, tune the MG-GPO settings to your needs. The settings of MG-GPO are located at the Configs section of the run.m script. They are:

problem = @ZDT2; % the objective funtion to be minimized
Nobj = 2;        % the number of the objectives
Nvar = 30;       % the number of the input variables
Npop = 10;       % the population size of each generation in MG-GPO
Ngen = 10;       % the total number of generations to evolve in MG-GPO
useGPML = 0;     % if use the GPML package
                 % set to 0 to use the local GP module with a cost of
                 % decreased performance
                 % set to 1 to use the GPML package with a better performance
                 % You should addpath for GPML package or run the startup.m
                 % script in the GPML package

Set problem to the name of your problem, and change Nobj and Nvar accordingly. The settings for Npop and Ngen depend on the problem to be optimized, but Npop = 80 and Ngen = 100 are usually a good starting point. Set useGPML = 1 to get the better performance of MG-GPO.

Access and visualize the history runs data

The data (all the varaible values at every generation) of each run will be saved as generation_*.mat file and put into the data/yyyymmdd_HHMMSS folder in the root directory, where the datetime string shows the starting time of the run. To visualize the evolution of the optmization process, in Matlab command line, run:

pl_solution 'data/yyyymmdd_HHMMSS' [[start] end]

This command will show the Pareto fronts from the start generation to the end generation for the run started at yyyymmdd_HHMMSS.

If start is not specified, the start generation is 1; If both the start and end are not specified, it will only show the Pareto front of the first generation in the run.

Here are some examples:

pl_solution 'data/20200610_004036' % visualize generation 1
pl_solution 'data/20200610_004036' 10 % visualize generation 1 to 10
pl_solution 'data/20200610_004036' 5 10 % visualize generation 5 to 10

For developers

WIP