A local-search heuristic for finding high-quality solutions for many hard optimization problems is explored. The method is inspired by recent progress in understanding far-from-equilibrium phenomena in terms of self-organized criticality, a concept introduced to describe emergent complexity in physical systems. This method, called extremal optimization, successively replaces the value of extremely undesirable variables in a sub-optimal solution with new, random ones. Large, avalanche-like fluctuations in the cost function self-organize from this dynamics, effectively scaling barriers to explore local optima in distant neighborhoods of the configuration space while eliminating the need to tune parameters. Drawing upon models used to simulate the dynamics of granular media, evolution, or geology, extremal optimization complements approximation methods inspired by equilibrium statistical physics, such as simulated annealing . This method is very general and so far has proved competitive with -- and even superior to -- more elaborate general-purpose heuristics on testbeds of constrained optimization problems with up to 10^5 variables, such as bipartitioning, coloring, and spin glasses. This heuristic is particularly successful near phase transitions, found in the parameter space of many optimization problems, which are deemed to be the origin of the hardest instances in terms of computational complexity. Analysis of a model problem predicts the only free parameter of the method in accordance with all experimental results. For (p)reprints, see
http://www.physics.emory.edu/faculty/boettcher/.