Optimizing at the ergodic edge

Boettcher, Stefan; Frank, Martin

doi:10.1016/j.physa.2005.10.034

Cited by 12 publications

(2 citation statements)

References 55 publications

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“…3) How to select the tuning parameter τ in τ-EO is an important problem to make τ-EO work properly. The studies [16], [22] show the correspondence of performance and the changes of parameter τ. The optimal tuning for internal parameter τ is another open topic.…”

Section: Discussionmentioning

confidence: 85%

Survey on computational complexity with phase transitions and extremal optimization

Zeng¹,

Lu²

2009

Proceedings of the 48h IEEE Conference on Decision and Control (CDC) Held Jointly With 2009 28th Chinese Control Conference

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Applying statistical mechanics to search problems in AI, decisions and optimization has been one of the powerful channels to solve NP-hard problems. Extensive analytical and experimental research has shown that the "phase transition" phenomenon in search space is often associated with the hardness of complexity. A Bak-Sneppen (BS) model based general-purpose heuristic method, called extremal optimization (EO), proposed by Boettcher and Percus from physics society may perform very well, especially near the phase transitions in compared with other optimization methods, e.g., genetic algorithm and simulated annealing, etc. To actuate more extensive investigations on this new optimization approach particularly in control, computer and optimization communities, this survey reviews the latest research results from fundamental to practice about the connection between computational complexity and phase transitions. Then, further introduces the concepts, fundamentals, algorithms and applications of EO from its capability of self-organized criticality, backbone analysis and co-evolution moving to a far-from-equilibrium state. Finally, the concluding remarks with suggested future research are illustrated.

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