Many space mission planning problems may be formulated as hybrid optimal control problems, i.e. problems that include both
continuous-valued variables and categorical (binary) variables. There may be thousands to millions of possible solutions;
a current practice is to pre-prune the categorical state space to limit the number of possible missions to a number that may
be evaluated via total enumeration. Of course this risks pruning away the optimal solution. The method developed here avoids
the need for pre-pruning by incorporating a new solution approach using nested genetic algorithms; an outer-loop genetic algorithm
that optimizes the categorical variable sequence and an inner-loop genetic algorithm that can use either a shape-based approximation
or a Lambert problem solver to quickly locate near-optimal solutions and return the cost to the outer-loop genetic algorithm.
This solution technique is tested on three asteroid tour missions of increasing complexity and is shown to yield near-optimal,
and possibly optimal, missions in many fewer evaluations than total enumeration would require.
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