Evolutionary dynamics of extremal optimization

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. This Extremal Optimization heuristic is evaluated theoretically and numerically.