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Evolutionary Dynamics of Extremal Optimization

  • Stefan Boettcher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5851)

Abstract

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.

Keywords

Local Search Spin Glass Spin Glass Model Extremal Optimization Local Search Process 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Stefan Boettcher
    • 1
  1. 1.Physics DepartmentEmory UniversityAtlantaUSA

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