A Fast Elitist Non-dominated Sorting Genetic Algorithm for Multi-objective Optimisation: NSGA-II
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
Modeling force response to small boat attack against high value commercial ships
WSC '05 Proceedings of the 37th conference on Winter simulation
Marine corps applicatons of data farming
WSC '05 Proceedings of the 37th conference on Winter simulation
Evolutionary Computation: Toward a New Philosophy of Machine Intelligence (IEEE Press Series on Computational Intelligence)
Proceedings of the 38th conference on Winter simulation
Automated red teaming: a proposed framework for military application
Proceedings of the 9th annual conference on Genetic and evolutionary computation
SpringSim '07 Proceedings of the 2007 spring simulation multiconference - Volume 2
Data farming around the world overview
Proceedings of the 40th Conference on Winter Simulation
Application of multi-objective bee colony optimization algorithm to automated red teaming
Winter Simulation Conference
Agents vs. pirates: multi-agent simulation and optimization to fight maritime piracy
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
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A maritime counter-piracy scenario is modeled using the agent-based simulation platform MANA. This simulation model is employed to investigate the requirements for defending a large commercial vessel, relying principally on non-lethal deterrents, against pirate hijacking. To assist this research, we utilize the data farming methodology to identify the "landscape of possibilities", i.e., data farming is employed to efficiently generate and examine a large range of simulation model variants which altogether depict a comprehensive overview of potential outcomes. Moreover, we complement this study through the evaluation of Automated Red Teaming (ART) to uncover the commercial vessel's critical vulnerabilities against pirates. ART differs from data farming by exploiting the principles of artificial evolution to automatically generate simulation model variants of interest. Both data farming and ART techniques are applied to our maritime counter-piracy simulation model in this paper. The experimental results provide complementary insights which may assist defense experts in future analyses and decision makings.