Mind-evolution-based machine learning and applications
Chengyi Sun, Yan Sun, Xie Keming · 2000
This work describes the foundation of proposing MEBML (mind-evolution-based machine learning) that was recently presented. Then the paper analyses the performance mechanism of MEBML which is different from GA (genetic algorithm), and its characteristics. With its own distinctive mechanism MEBML improved the efficiency and convergence rate greatly compared with GA. The paper also summarizes the recent development of researches on MEBML, which includes several different strategies of similarity and dissimilarity, the proof of convergence of MEBML and its applications. In addition the authors discuss the future work of MEBML.