Welcome to desdeo-emo’s documentation!

The evolutionary algorithms package within the desdeo framework.

Currently supported:

  • Multi-objective optimization with visualization and interaction support.

  • Preference is accepted as a reference point.

  • Surrogate modelling (neural networks and genetic trees) evolved via EAs.

  • Surrogate assisted optimization

  • Constraint handling using RVEA

  • IOPIS optimization using RVEA and NSGA-III

Currently NOT supported:

  • Binary and integer variables.

To test the code, open the binder link and read example.ipynb.

Requirements

See pyproject.toml for Python package requirements.

Installation

To install and use this package on a *nix-based system, follow one of the following procedures.

For users

First, create a new virtual environment for the project. Then install the package using the following command:

$ pip install desdeo-emo

For developers

Download the code or clone it with the following command:

$ git clone https://github.com/industrial-optimization-group/desdeo-emo

Then, create a new virtual environment for the project and install the package in it:

$ cd desdeo-emo
$ poetry init
$ poetry install

Currently implemented methods

Algorithm

Reference

RVEA

R. Cheng, Y. Jin, M. Olhofer and B. Sendhoff, A Reference Vector Guided Evolutionary Algorithm for Many-objective Optimization, IEEE Transactions on Evolutionary Computation, 2016

NSGA-III

K. Deb and H. Jain, “An Evolutionary Many-Objective Optimization Algorithm Using Reference-Point-Based Nondominated Sorting Approach, Part I: Solving Problems With Box Constraints,” in IEEE Transactions on Evolutionary Computation, vol. 18, no. 4, pp. 577-601, Aug. 2014, doi: 10.1109/TEVC.2013.2281535.

MOEA/D

Q. Zhang and H. Li, “MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition,” in IEEE Transactions on Evolutionary Computation, vol. 11, no. 6, pp. 712-731, Dec. 2007, doi: 10.1109/TEVC.2007.892759.

PPGA

Laumanns, M., Rudolph, G., & Schwefel, H. P. (1998). A spatial predator-prey approach to multi-objective optimization: A preliminary study. In International Conference on Parallel Problem Solving from Nature (pp. 241-249). Springer, Berlin, Heidelberg.

IOPIS-RVEA

Saini B.S., Hakanen J., Miettinen K. (2020) A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In: Bäck T. et al. (eds) Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science, vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_17

IOPIS-NSGA-III

Saini B.S., Hakanen J., Miettinen K. (2020) A New Paradigm in Interactive Evolutionary Multiobjective Optimization. In: Bäck T. et al. (eds) Parallel Problem Solving from Nature – PPSN XVI. PPSN 2020. Lecture Notes in Computer Science, vol 12270. Springer, Cham. https://doi.org/10.1007/978-3-030-58115-2_17

Indices and tables