This repository contains the Matlab implementation of our work Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization.

The demo.m file implements a toy example in the paper, in which the source tasks are one-max and one-min, and the target task is trap-5.

In MOO\, we have included examples for AMT-enabled NSGA-II.

In Knapsack\, we have included examples for Knapsack problems.

@ARTICLE{da18tcyb,
  author={B. Da and A. Gupta and Y. S. Ong},
  journal={IEEE Transactions on Cybernetics}, 
  title={Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization}, 
  year={2018}, 
  volume={}, 
  number={}, 
  pages={1-14}, 
  doi={10.1109/TCYB.2018.2864345}, 
  ISSN={2168-2267}, 
  month={},
}

Added Python implementation in ./AMETEA_python.