A technique based on Evolutionary Computational Methods (ECMs) was developed that allows for the automated optimization of complex computationally modeled systems, such as autonomous systems. The primary technology, which enables the ECM to find optimal solutions in complex search spaces, derives from evolutionary algorithms such as the genetic algorithm and differential evolution. These methods are based on biological processes, particularly genetics, and define an iterative process that evolves parameter sets into an optimum.
Evolutionary computation is a method that operates on a population of existing computational-based engineering models (or simulators) and competes them using biologically inspired genetic operators on large parallel cluster computers. The result is the ability to automatically find design optimizations and trades, and thereby greatly amplify the role of the system engineer.
The ECM technique is extremely effective at quickly finding not only individual best solutions, but also an entire collection of best solutions for all possible ranges of conditions. ECM provides an efficient standard of performance that specifies the best solution given a set of requirements or goals for all possible environmental variables. The ECM derived standard of performance can be directly compared to actual performance to provide a test standard of reference. Comparison of the ECM best solutions with actual performance will identify where the deviations from optimum occurred leading to localization of problem areas. This includes illuminating deficiencies in simulation capabilities. Examination of the full set of ECM derived best performance will enable the identification and characterization of unexpected or emergent behaviors.