Monte-Carlo tuning with Genetic Algorithms and MPI for minimum bias events at the LHC

Monte-Carlo generators are essential tools in high energy physics. We use them on various points such as background estimation, trigger design and new physics searches. Because of our understanding of physics and the technical challenges they usually implement models which have some tunable parameters. By fine-tuning these parameters description of the data can be improved and more precise measurements can be made. Soft interactions called minimum bias events are one of the such places where tuning of several parameters are required. With approximately 20 minimum bias interactions at each bunch crossing at full luminosity at the LHC, it is crucial that we understand them in order to study interesting events. In this talk we present a new approach to Monte-Carlo tuning by using Genetic Algorithms and distributed analysis with Message Passing Interface and give an example of tuning Pythia for minimum bias events.