adasamp-pareto

Adaptive optimization of black-box multi-objective optimization problems with binary constraints using machine learning.

https://github.com/raoulheese/adasamp-pareto

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Adaptive optimization of black-box multi-objective optimization problems with binary constraints using machine learning.

README.rst

************************************************************************
Adaptive bayes-sampling for multi-criteria optimization (adasamp-pareto)
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Adaptive optimization algorithm for black-box multi-objective optimization problems with binary constraints on the foundation of Bayes optimization. The algorithm aims to find the Pareto-optimal solution of

.. math::

   max [ y(x) ] s.t. f(x) = feasible
   
in an iterative procedure. Here, :math:`y(x)` denotes the multi-dimensional goals and :math:`f(x)` the binary feasibility of the problem (in the sense that certain design variables :math:`x` lead to invalid goals). All technical details can be found in the paper "Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification" (``_).

**Installation**

Install via ``pip`` or clone this repository. In order to use ``pip``, type:

.. code-block:: sh

    $ pip install adasamp-pareto
	
**Usage**

The class ``AdaptiveSampler`` is used to define and solve a problem instance. Simple example:

.. code-block:: python

  from adasamp import AdaptiveSampler

  # Create instance
  sampler = AdaptiveSampler(func,       # Problem definition: function returns (goals Y, feasibility f)
                            X_limits,   # Design variable limits to search solution in
                            Y_ref,      # Reference point, has to be dominated by any goal Y
                            iterations, # Number of solver iterations
                            Y_model,    # Regression model to predict goals Y
                            f_model)    # Classification model to predict feasibility f

  # Return the sampling suggestions X, the corresponding goals Y, and the corresponding feasibilities f.
  X, Y, f = sampler.sample()
  
Demo notebooks can be found in the `examples/` directory.
  
**Documentation**

Complete documentation is available: ``_.

📖 **Citation**

If you find this code useful in your research, please consider citing:

.. code-block::

    @misc{heesebortzCITE2020,
          title={Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification}, 
          author={Raoul Heese and Michael Bortz},
          year={2020},
          eprint={2008.12005},
          archivePrefix={arXiv},
          primaryClass={stat.ML}
         }

*This project is currently not under development and is not actively maintained.*

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Citation (CITATION.bib)

@misc{heesebortzCITE2020,
      title={Adaptive Sampling of Pareto Frontiers with Binary Constraints Using Regression and Classification}, 
      author={Raoul Heese and Michael Bortz},
      year={2020},
      eprint={2008.12005},
      archivePrefix={arXiv},
      primaryClass={stat.ML}
     }

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