Evolutionary computation for the ARIEL mission planning tool

The ARIEL mission main goal is the measurement of atmospheres of transiting planets. This requires the observation of two types of events: primary and secondary eclipses. In order to yield measurements of sufficient Signal-to-Noise Ratio to fulfill the mission objectives, the events of each exoplanet have to be observed several times. In addition, several criteria have to be considered to carry out each observation, such as the exoplanet visibility, its event duration, its potential significance in the survey, and no overlapping with other tasks. Consequently, obtaining a long term mission plan becomes unaffordable for human planners due to the complexity of computing the huge number of possible combinations for finding an optimum solution. In this contribution we present a mission planning tool based on Evolutionary Algorithms, which are focused on solving optimization problems such as the planning of several tasks. Specifically, the proposed tool finds a solution that highly optimizes the defined objectives, which are based on the maximization of the time spent on scientific observations and the scientific return. The results obtained on the large experimental set up support that the proposed scheduler technology is robust and can function in a variety of scenarios, offering a competitive performance which does not depend on the collection of exoplanets to be observed.