Bacterial Foraging Optimization

In the April 2012 issue of MSDN Magazine I describe a fascinating artificial intelligence technique called Bacterial Foraging Optimization (BFO). BFO is a meta-heuristic (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 Calculus-based) technique. The BFO article is available online at: 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 chemo-sensing determines the quality of the current position/solution. BFO is an alternative to other meta-heuristics based on the behavior of natural systems, in particular real-valued genetic algorithms and particle swarm optimization.

This entry was posted in Machine Learning. Bookmark the permalink.