Nature's way of optimizing

We propose a general-purpose method for finding high-quality solutions to h ard optimization problems, inspired by self-organizing processes often foun d in nature. The method, called Extremal Optimization, successively elimina tes extremely undesirable components of sub-optimal solutions. Drawing upon models used to simulate far-from-equilibrium dynamics, it complements appr oximation methods inspired by equilibrium statistical physics, such as Simu lated Annealing. With only one adjustable parameter, its performance proves competitive with, and often superior to, more elaborate stochastic optimiz ation procedures. We demonstrate it here on two classic hard optimization p roblems: graph partitioning and the traveling salesman problem. (C) 2000 El sevier Science B.V. All rights reserved.