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. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Boettcher, S. (2005). Self-organizing dynamics for optimization. In Lecture Notes in Computer Science (Vol. 3515, pp. 386–394). Springer Verlag. https://doi.org/10.1007/11428848_52
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