Self-organizing dynamics for optimization

Authors:
Stefan Boettcher
Affiliations:
Physics Department, Emory University, Atlanta, Georgia
Venue:
ICCS'05 Proceedings of the 5th international conference on Computational Science - Volume Part II
Year:
2005

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Abstract

Motivated by noise-driven cellular automata models of self-organized criticality (SOC), a new paradigm for the treatment of hard combinatorial optimization problems is proposed. An extremal selection process preferentially advances variables in a poor local state. The ensuing dynamic process creates broad fluctuations to explore energy landscapes widely, with frequent returns to near-optimal configurations.