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 citation (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)}
}The paper descibes the UPSO-QP approach.
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, University of Patras, Greece.
