Evolving UCAV Strategies Through Simulated Evolution

An article from the Wired Magazine Danger Room section outlines five top national security research challenges for the next president of United States. See it here.

The author says military application of genetic algorithms is an important one when it comes to UCAVs.

"Applications for Genetic Algorithms in Battlefield Operations - This is a natural research progression for an armed forces increasingly willing to conduct operation with unmanned aerial vehicles, or UAVs. Genetic computing and algorithms allow machines to learn through repeated trial and error, as programs can "evolve" to solve extremely difficult artificial intelligence problems. This has very clear applications for battlefield operations. For example, UAVs can be freed to develop the most efficient routes for surveillance, an experiment that has already shown some success. Genetic computing has also shown promise in forecast modeling, and additional research should be conducted to investigate its application to modeling scenarios with national security implications."

It is not a novelty, since military evolutionary computation applications date back to, at least, 1980.

For example, this article from 1991 deals with the optimization of thrust vectoring nozzles using a genetic algorithm. Click here to see the first page. BUT, nozzle optimization dates back to late 1960s and early 1970s, as demonstraded by Professor Hans-Paul Schwefel pioneer work.