Nature's Way of Optimizing

Details Nature's Way of Optimizing Nature's Way of Optimizing

1999-01-29

arxiv.org/abs/cond-mat/9901351v2

Artificial Intelligence, 119 (2000) 275

Physics

Condensed Matter

Statistical Mechanics

15 Pages, RevTex, 5 eps-figures included, several additions, as to appear in Artificial Intelligence; related papers available

Scientific paper

We propose a general-purpose method for finding high-quality solutions to hard optimization problems, inspired by self-organizing processes often found in nature. The method, called Extremal Optimization, successively eliminates extremely undesirable components of sub-optimal solutions. Drawing upon models used to simulate far-from-equilibrium dynamics, it complements approximation methods inspired by equilibrium statistical physics, such as Simulated Annealing. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimization procedures. We demonstrate it here on two classic hard optimization problems: graph partitioning and the traveling salesman problem.

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