Computational Optimization and Innovation Laboratory was founded at Michigan State University in the early 2013. Our reseach interests include solving various types of Optimization Problems, Modeling, Machine Learning using Evolutionary Algorithms, Meta-modeling, Bi-level optmization and Innovization. At COIN we are interested in both the theorititcal foundations and the application of these topics to Engineering, Sciences and Business. Our research group includes a diverse set of researchers from Computer Science, Electrical Engineering and Mechanical Engineering among others.
Yashesh left COINLab and has recently started his post Ph.D. career. We had a virtual Get-together to give him a memorable send-off. We had an awesome time with him since he had joined MSU. We wish you all the best and very successful in your new chapter of life!
Proteek left COINLab and will start his post PhD career. Before a new chapter in his life begins, we had a Get-together to give him a memorable send-off. He has been at MSU since Fall of 2015 and we had an awesome time with him. We wish you all the best with your new job and great success.
We are glad to announce pymoo 0.3.1 has been released. Many new features were added, for instance visualization and decision making: pymoo.org
Paper titled "NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm" by Zhichao Lu et. al. has been recognized as the best-paper in The Genetic and Evolutionary Computation Conference 2019 under EML Track. The research focuses at evolving architectures of Deep Convolution Neural Network by simultaneously considering two conflicting objectives.
Paper titled "Simulation Optimization of Water Usage and Crop Yield Using Precision Irrigation" by Proteek Chandan Roy et. al. has been recognized as the best-poster at 10th International Conference on Evolutionary Multi-Criterion Optimization. The research focuses on optimizing crop yield prediction with minimal water usage using Subsurface Water Retention Technology and predicting the best irrigation schedule. The simulation uses DSSAT and HYDRUS-2D to predict crop yield with different configurations of SWRT membrane.