In the April 2012 issue of MSDN Magazine I describe a fascinating artificial intelligence technique called Bacterial Foraging Optimization (BFO). BFO is a metaheuristic (a general set of guidelines) that models the behavior of bacteria such as E. coli to find approximate solutions to numerical optimization problems in situations where there is no practical classical (like Calculusbased) technique. The BFO article is available online at: http://msdn.microsoft.com/enus/magazine/hh882453.aspx. The essence of a BFO algorithm is that there are several simulated bacteria. The position of each simulated bacterium represents a possible solution to the problem you’re trying to optimize. Bacteria movement generates new solutions. Bacteria chemosensing determines the quality of the current position/solution. BFO is an alternative to other metaheuristics based on the behavior of natural systems, in particular realvalued genetic algorithms and particle swarm optimization.
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