Code release for the paper Achieving Diversity in Objective Space for Sample-Efficient Search of Multiobjective Optimization Problems, published in Winter Sim 2022. The paper and this code release develop a method similar to active search. The method allows users to input a multiobjective design or optimization problem and obtain a set of diverse solutions. The diversity of this solution set is important to a range of scientific and engineering applications.
Run python setup.py install in the command line.
The src/ directory is subdivided as follows:
src/acquisitions/contains implementations of all acquisition functions.src/model/contains our GP implementation. By default, this implementation uses a constant mean function and the Matern 5/2 kernel.src/runners/contains the BO runners, which are invoked to run a full BO loop.src/test_problems/contains implementations of synthetic functions, which are used to benchmark BO acquisition functions.
We have a few simple demos in the demos/ folder, which you should run to get started.