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Published in:
2009 | OriginalPaper | Chapter
Evolutionary Dynamics of Extremal Optimization
Author : Stefan Boettcher
Published in: Learning and Intelligent Optimization
Publisher: Springer Berlin Heidelberg
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Dynamic features of the recently introduced extremal optimization heuristic are analyzed. Numerical studies of this evolutionary search heuristic show that it performs optimally at a transition between a jammed and an diffusive state. Using a simple, annealed model, some of the key features of extremal optimization are explained. In particular, it is verified that the dynamics of local search possesses a generic critical point under the variation of its sole parameter, separating phases of too greedy (non-ergodic, jammed) and too random (ergodic, diffusive) exploration. Analytic comparison with other local search methods, such as a fixed temperature Metropolis algorithm, within this model suggests that the existence of the critical point is the essential distinction leading to the optimal performance of the extremal optimization heuristic.