Clever Algorithms: Nature-Inspired Programming Recipes

By Jason Brownlee PhD

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Physical Algorithms

Overview

This chapter describes Physical Algorithms.

Physical Properties

Physical algorithms are those algorithms inspired by a physical process. The described physical algorithm generally belong to the fields of Metaheustics and Computational Intelligence, although do not fit neatly into the existing categories of the biological inspired techniques (such as Swarm, Immune, Neural, and Evolution). In this vein, they could just as easily be referred to as nature inspired algorithms.

The inspiring physical systems range from metallurgy, music, the interplay between culture and evolution, and complex dynamic systems such as avalanches. They are generally stochastic optimization algorithms with a mixtures of local (neighborhood-based) and global search techniques.

Algorithms

Extensions

There are many other algorithms and classes of algorithm that were not described inspired by natural systems, not limited to:

  • More Annealing: Extensions to the classical Simulated Annealing algorithm, such as Adaptive Simulated Annealing (formally Very Fast Simulated Re-annealing) [Ingber1989] [Ingber1996], and Quantum Annealing [Apolloni1989] [Das2005].
  • Stochastic tunneling: based on the physical idea of a particle tunneling through structures [Wenzel1999].

Bibliography

[Apolloni1989] B. Apolloni and C. Caravalho and D. De Falco, "Quantum stochastic optimization", Stochastic Processes and their Applications, 1989.
[Das2005] A. Das and B. K. Chakrabarti, "Quantum annealing and related optimization methods", Springer, 2005.
[Ingber1989] L. Ingber, "Very fast simulated re-annealing", Mathematical and Computer Modelling, 1989.
[Ingber1996] L. Ingber, "Adaptive simulated annealing (ASA): Lessons learned", Control and Cybernetics, 1996.
[Wenzel1999] W. Wenzel and K. Hamacher, "A Stochastic Tunneling Approach for Global Minimization of Complex Potential Energy Landscapes", Phys. Rev. Lett., 1999.
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