Scheduling observations of agile satellites with combined genetic algorithm

This paper describes a combined genetic algorithm for selecting and scheduling tasks of agile earth observing satellites (AEOS). This kind of satellites has three degrees of freedom for acquiring images, and giving opportunities for a more efficient use of the satellite imaging capabilities. But the selection and scheduling of observations becomes significantly difficult, due to the larger search space for potential solutions. Hence, selecting and scheduling observations of agile satellites is a highly combinatorial problem. Inspired by successful commercial applications of evolutionary algorithms in scheduling domains, this paper presents work in progress regarding the use of combined genetic algorithm to solve it. Both the model problems and the algorithm are described. The validity of this approach is validated by emulations.