Nature's way of optimizing

Nature's way of optimizing

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Article ID: iaor2002419
Country: United States
Volume: 119
Issue: 1/2
Start Page Number: 275
End Page Number: 286
Publication Date: May 2000
Journal: Artificial Intelligence
Authors: ,
Abstract:

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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