The simulation of land combat operations is a complex task. The space of possibilities is exponential and the performance criteria are usually in conflict; thus finding a sweet spot in this complex search space is a hard task. This paper focuses on the effect of population size and mutation rate on the performance of NSGA–II, as the evolutionary multiobjective optimization technique, to decide on the composition of forces using a complex land combat multi-agent scenario planning tool.
BT - Simulated Evolution and Learning LA - eng N2 -
The simulation of land combat operations is a complex task. The space of possibilities is exponential and the performance criteria are usually in conflict; thus finding a sweet spot in this complex search space is a hard task. This paper focuses on the effect of population size and mutation rate on the performance of NSGA–II, as the evolutionary multiobjective optimization technique, to decide on the composition of forces using a complex land combat multi-agent scenario planning tool.
PB - Springer Berlin Heidelberg PY - 2006 T2 - Simulated Evolution and Learning TI - Land Combat Scenario Planning: A Multiobjective Approach ER -