Efficient (almost) single file implementations of evolutionary algorithms.
- Particle Swarm Optimization (UPSO)
- Gradient-Assisted Particle Swarm Optimization for Constrained Optimization (UPSO-QP)
- Particle Swarm Optimization with Penalties for Constrained Optimization (UPSO-Penalty)
- Particle Swarm Optimization with Gradient Repair Scheme (UPSO-Grad)
- Differential Evolution (DE)
- MAP-Elites (CVT-MAP-Elites)
- Uncertain MAP-Elites (preliminary: works only for noisy objective function, not noisy features)
- improved Cross Entropy Method (iCEM)
./waf configure [--prefix=PATH_TO_INSTALL]./waf[sudo] ./waf install
There are numerous examples under src/examples. If the compilation procedure has completed successfully, you can run them by ./build/example_name.
If you use AlgEvo in a scientific publication, please use the following citations (pdf, pdf:
@inproceedings{chatzilygeroudis2023lion,
title={Fast and Robust Constrained Optimization via Evolutionary and Quadratic Programming},
author={Chatzilygeroudis, Konstantinos and Vrahatis, Michael},
year={2023},
booktitle={The 17th Learning and Intelligent Optimization Conference (LION)}
}This paper descibes the UPSO-QP approach.
@article{printzios2026hybrid,
title={Hybrid Augmented Lagrangian Method for General Constrained Optimization via Evolutionary Algorithms},
author={Printzios, Lampros and Chatzilygeroudis, Konstantinos},
journal={arXiv preprint arXiv:2607.16876},
year={2026}
}This paper describes the Hybrid Augmented Lagrangian Method (HALM), more information here.
This work was supported by the Hellenic Foundation for Research and Innovation (H.F.R.I.) under the "3rd Call for H.F.R.I. Research Projects to support Post-Doctoral Researchers" (Project Acronym: NOSALRO, Project Number: 7541).
This work was conducted within the Computational Intelligence Lab (CILab), Department of Mathematics, and the Laboratory of Automation and Robotics (LAR), Department of Electrical & Computer Engineering, University of Patras, Greece.
