Solar flare prediction by SVM integrated CBGA with dynamic mutation rate

Solar activity has various influences on the global environment, in particular on the magnetic storm and the likelihood of natural disasters. Specifically, it may have serious impacts on the Earth such as failure of satellite communication and navigation (GPS), satellite damage, increased radiation exposure to astronauts, geomagnetic storm and aurora, and power plant failures causing more serious disaster. For a precise forecast of larger scale solar flares causing serious disaster, it is important to improve the space weather forecast, which is basically a daily forecast of the solar flare. In our work so far, a machine-learning algorithm called Support Vector Machine (SVM) was used to forecast the space weather. Here, we propose to extend this technology by integrating Case Based Genetic Algorithm (CBGA) for a more precise forecast and present an evaluation of this approach. Experimental evaluation shows that triple mutation rate on the slowdown of evolution in our Genetic Algorithm improves considerably (e.g. another 5%) more than original mutation rate in the True Skill Statistics TSS.