Extending the SVM integration with case based restarting GA to predict solar flare
Yoshihiko Kubota, Setsuo Tsuruta, Takayuki Muranushi, Yuko Hada-Muranushi, Rainer Knauf, Yoshitaka Sakurai, Ernesto Damiani · 2017
Human life can be seriously affected by unusual high solar flare. It causes serious problems such as destroying satellites, damaging electric power plants, etc. Predicting solar flare peaks is indispensable. Support Vector Machine (SVM) was used to predict the solar flare intensity based on data of the past. However, such prediction is an extremely difficult imbalanced classification problem causing a very large scale combinatorial problem. To overcome this, a local optimizer such as SVM was cooperatively or synergistically integrated with a case base and GA. This extension was called Case Based Genetic Algorithm with Local Optimizer (CBGALO). This synergetic technology outperformed around 10% over SVM only. However, GA is known for a tendency to fall into stagnation or local optima without improving. This paper presents a further extended strategy which introduces GA restarting with various operations such as mutation rate etc. especially with various but high quality cases used as the initial population at each restart. This highly synergetic technology is called CBRSGALO (Case Based integration of ReStart GA with Local Optimizer). Experimental evaluation showed that CBRSGALO outperformed around 20% over just a local optimizer such as SVM.