We advocate exploring complex models by combining data miners (to find a small set of most critical examples) and of multiobjective optimizers (that focus on those critical examples). An example of such a combination is the GALE optimizer that intelligently explores thousands of scenarios by examining just a few dozen of the most informative examples. GALE-style reasoning enables a very fast, very wide ranging exploration of behaviors, as well as the effects of those behaviors' limitations. This paper applies GALE to the continuous descent approach (CDA) model within the Georgia Tech Work Models that Compute framework. CDA is a model of pilot interactions: with each other and also with the navigation systems critical to safe flight. We show that, using CDA+GALE, it is possible to identify and mitigate factors that make pilots unable to complete all their required tasks in the context of different 1) function allocation strategies, 2) pilot cognitive control strategies, and 3) operational contexts that impact and safe aircraft operation. We also show that other optimization methods can be so slow to run that, without GALE, it might be impractical to find those mitigations.

Multi-objective evolutionary algorithms (MOEAs) help software engineers find novel solutions to complex problems. When automatic tools explore too many options, they are slow to use and hard to comprehend. GALE is a near-linear time MOEA that builds a piecewise approximation to the surface of best solutions along the Pareto frontier. For each piece, GALE mutates solutions towards the better end. In numerous case studies, GALE finds comparable solutions to standard methods (NSGA-II, SPEA2) using far fewer evaluations (e.g. 20 evaluations, not 1,000). GALE is recommended when a model is expensive to evaluate, or when some audience needs to browse and understand how an MOEA has made its conclusions.