MOO Easy Tool

An user friendly MOO tool

Main algorithm:

1. Initialize GP model:

Attr	Default-Value	Description
O	non-default	# of objective to optimize
C	non-default	# of constrains (TBD)
d	non-default	input space dimensions
kernel	non-default	kernel of the GP
X	Empty 2D np array	input data of the GP
Y	Empty 2D np array	output data of the GP
noise_variance	0.01	output noise of the GP
opt	gpflow.optimizers.Scipy()	Optimizer of GP's kernel
multiGPR	None	Gaussian Process Regressor

NOTE1: We need at least 1 sample (x,y) so that GPR is not completly flat without any further assumption. NOTE2: Minimum noise is 1e-6 which is practically none

2. Get at least 1 random sample (intput sample):

   1. Generate a random intput sample
   2. Evaluate functions and constrains to get its ourput
   3. Add the sample (input, output) to GP model
   4. Update multiGPR model
   5. Optimize multiGPR's kernel hyperparameters

3. For each iteration of the iterations:

   1. Create a searching grid of the input space.
   2. Evaluate acquisition function in the grid to get the optimum.
   3. Add the sample (input, output) to GP model
   4. Update multiGPR model
   5. Optimize multiGPR's kernel hyperparameters

TODO: Next task to complete

Released:

• ISSUE: GaussianProcess.plotSamples functions for >1 input dimension
• Write output files of the experiments

Working:

• Implement cmd parameters
• Study to fix 3.4 and 3.5 at the start of step 3
• Final result, pareto front and pareto set

Planning:

• Code efficient benchmark functions and separate them from main

Backlog:

• More efficient search of acquisition function optimum?

• Several samples for each iteration?

• Implement constrains

• Improve usage of bounds (each input variable its own bound)

  • NOTE: Transform input variables instead of bounds?

• Code and add many acquisition functions

• Return values that u have not evaluated as pareto front