The genetic processing approach is also easy to modify as ATO requirements and goals change. Using the alternative solution approach—linear programming—software engineers must rewrite the equations defining the system when requirements change. In the worst case, the new requirements may even change the problem beyond a linear state. Genetic processing, due to its dynamic nature, is thus a bettersuited approach for solving problems with changing requirements—the very nature of a combat environment.

The ATO-Link and ATO-Stream packages augment the genetic algorithm framework, with the addition of expert system rules and conventional deterministic algorithms that result in improved processing speed and better results. At the end of a genetic algorithm’s execution time, for example, planners can insert other, deterministic algorithms to assign any remaining unserviced targets to similarly unassigned resources. They can also inject such deterministic algorithms and rules to seed the genetic algorithm’s first generation with reasonable solutions and to repopulate the solutions that die off each generation.

The ATO-Link and ATO-Stream software systems output a valid ATO that honors all imposed constraints and is tailored to the MAAP chief’s objectives. Generating the optimal solution to the ATO problem is “NPhard” (a classification of the complexity of an algorithm employed in solving an NP, or nondeterministic polynomial, time problem). Therefore, it is unreasonable to expect the generation of an optimal ATO in a time frame suitable for the battlefield. Rather than seeking the optimal ATO, planners can instead specify the execution time of the genetic routine and quickly generate a “good” solution—an important strength of the genetic processing approach. Depending on the time available, the user can specify a 5 min, or even a 45 min, solution. In general, the longer operators allow the system to process, the more optimal the resulting ATO will be. Nevertheless, stopping system execution at any point beyond the shortest processing period still produces a complete, valid ATO solution optimized for the allotted execution time. With most linear programming algorithms, the flexibility of variable execution time is not available because there is an unknown, situation- or data-dependent, amount of computation time that yields a single solution rather than intermediate results.