Co-evolving parasites improve simulated evolution as an optimization procedure
CNLS '89 Proceedings of the ninth annual international conference of the Center for Nonlinear Studies on Self-organizing, Collective, and Cooperative Phenomena in Natural and Artificial Computing Networks on Emergent computation
Genetic Algorithms for Machine Learning
Genetic Algorithms for Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
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The focus of warfare has shifted from the Industrial Age to the Information Age, as encapsulated by the term Network Enabled Capability. This emphasises information sharing, command decision-making, and the resultant plans made by commanders on the basis of that information. Planning by a higher level military commander is, in most cases, regarded as such a difficult process to emulate, that it is performed by a real commander during wargaming or during an experimental session based on a Synthetic Environment. Such an approach gives a rich representation of a small number of data points. However, a more complete analysis should allow search across a wider set of alternatives. This requires a closed-form version of such a simulation. In this paper, we discuss an approach to this problem, based on emulating the higher command process using a combination of game theory and genetic algorithms. This process was initially implemented in an exploratory research initiative, described here, and now forms the basis of the development of a "Mission Planner," potentially applicable to all of our higher level closed-form simulation models.