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A New Hybrid Metaheuristic – Combining Stochastic Tunneling and Energy Landscape Paving

  • Kay Hamacher
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7919)

Abstract

(Hybrid) metaheuristics such as simulated annealing, genetic algorithms, or extremal optimization play a most prominent role in global optimization. The performance of these algorithms and their respective sampling behavior during the search process are themselves interesting problems. Here, we show that a combination of two approaches – namely Energy Landscape Paving (ELP) and Stochastic Tunneling (STUN) – can overcome known problems of other Metropolis-sampling-based procedures. We show on grounds of non-equilibrium statistical mechanics and empirical evidence on the synergistic advantages of this combined approach and discuss simulations for a complex optimization problem.

Keywords

Simulated Annealing Global Optimization Tabu Search Spin Glass Ising Spin 
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 2013

Authors and Affiliations

  • Kay Hamacher
    • 1
  1. 1.Dept. of Computer Science, Dept. of Physics & Dept. of BiologyTechnical University DarmstadtDarmstadtGermany

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