Adaptive Model-based Transfer for Binary problems
This repository contains the Matlab implementation of our work Curbing Negative Influences Online for Seamless Transfer Evolutionary Optimization.
Usage
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.
Citation
@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={}, }
Update
Added Python implementation in ./AMETEA_python.