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Evolutionary Dynamics of Extremal Optimization

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Book cover Learning and Intelligent Optimization (LION 2009)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 5851))

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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.

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Authors and Affiliations

  1. Physics Department, Emory University, Atlanta, USA

    Stefan Boettcher

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  1. Stefan Boettcher
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Editors and Affiliations

  1. IRIDIA, CoDE, Université Libre de Bruxelles, Avenue F. Roosevelt 50, CP 194/6, 1050, Brussels, Belgium

    Thomas Stützle

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Boettcher, S. (2009). Evolutionary Dynamics of Extremal Optimization. In: Stützle, T. (eds) Learning and Intelligent Optimization. LION 2009. Lecture Notes in Computer Science, vol 5851. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-11169-3_1

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  • DOI: https://doi.org/10.1007/978-3-642-11169-3_1

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-11168-6

  • Online ISBN: 978-3-642-11169-3

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