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This paper presents novel area coverage algorithms that have been validated using Boeing VSTL hardware. Even though the multi-vehicle search area coverage problem is large and complex, several new memetic computing methods have been presented that decompose, allocate and optimize the exploration of a search area for multiple heterogeneous vehicles. These new methods were shown to have good performance and quality, and as they are defined in a general way, these methods are applicable to many other problem domains. The methods have been combined into a mission-planner architecture that is able to adaptively control the behavior of multiple vehicles with dynamic vehicle capabilities and environments for mission assurance. The topic of mission-planning architectures and optimization of swarms of autonomous vehicles is a young and exciting field with many opportunities for research. More computationally efficient methods for decomposition may be useful, as well as the application of next-generation meta-learning architectures for path planning. In addition to the existing collision avoidance, path de-confliction during planning can improve safety and efficiency.