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We present a general-purpose optimization algorithm inspired by "run-and-tumble", the biased random walk chemotactic swimming strategy used by the bacterium Escherichia coli to locate regions of high nutrient concentration The method uses particles (corresponding to bacteria) that swim through the variable space (corresponding to the attractant concentration profile). By constantly performing temporal comparisons, the particles drift towards the minimum or maximum of the function of interest. We illustrate the use of our method with four examples. We also present a discrete version of the algorithm. The new algorithm is expected to be useful in combinatorial optimization problems involving many variables, where the functional landscape is apparently stochastic and has local minima, but preserves some derivative structure at intermediate scales.

Original publication

DOI

10.1016/j.biosystems.2008.05.009

Type

Journal article

Journal

Biosystems

Publication Date

10/2008

Volume

94

Pages

47 - 54

Keywords

Algorithms, Chemotaxis, Computational Biology, Computer Simulation, Escherichia coli, Movement, Stochastic Processes