Evolutionary Optimization in Dynamic Environments
Evolutionary Optimization in Dynamic Environments
Cooperative Coevolution: An Architecture for Evolving Coadapted Subcomponents
Evolutionary Computation
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Evolutionary Algorithms for Solving Multi-Objective Problems (Genetic and Evolutionary Computation)
Learning with case-injected genetic algorithms
IEEE Transactions on Evolutionary Computation
Enhancing automated red teaming with evolvable simulation
Proceedings of the first ACM/SIGEVO Summit on Genetic and Evolutionary Computation
Evolutionary computation in the undergraduate curriculum
Journal of Computing Sciences in Colleges
Application of multi-objective bee colony optimization algorithm to automated red teaming
Winter Simulation Conference
Coevolving collection plans for UAS constellations
Proceedings of the 13th annual conference on Genetic and evolutionary computation
Learning belief connections in a model for situation awareness
PRIMA'11 Proceedings of the 14th international conference on Agents in Principle, Agents in Practice
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Air operations mission planning is a complex task, growing ever more complex as the number, variety, and interactivity of air assets increases. Mission planners are responsible for generating as close to optimal taskings of air assets to missions under severe time constraints. This function can be aided by decision-support tools to help ease the search process through automation. This paper presents several applications of multi-objective evolutionary algorithms for discovering suitable plans in the air operations domain, including dynamic targeting for air strike assets, intelligence, surveillance, and reconnaissance (ISR) asset mission planning, and unmanned aerial systems (UAS) planning. Lessons learned from these studies are described and an exploration of potential future directions is discussed.