Extremal optimization dynamics in neutral landscapes: the royal road case

  • Authors:

  • I. De Falco;A. Della Cioppa;D. Maisto;U. Scafuri;E. Tarantino

  • Affiliations:

  • Institute of High Performance Computing and Networking, National Research Council of Italy, ICAR, Naples, Italy;Natural Computation Lab, DIIIE, University of Salerno, Fisciano, SA, Italy;Institute of High Performance Computing and Networking, National Research Council of Italy, ICAR, Naples, Italy;Institute of High Performance Computing and Networking, National Research Council of Italy, ICAR, Naples, Italy;Institute of High Performance Computing and Networking, National Research Council of Italy, ICAR, Naples, Italy

  • Venue:
  • EA'09 Proceedings of the 9th international conference on Artificial evolution
  • Year:
  • 2009

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Abstract

In recent years a new view of evolutionary dynamics has emerged based on both neutrality and balance between adaptation and exaptation. Differently from the canonical adaptive paradigm where the genotypic variability is strictly related to the change at fitness level, such a paradigm has raised awareness of the importance of both selective neutrality and co-option by exaptation. This paper investigates an innovative method based on Extremal Optimization, a coevolutionary algorithm successfully applied to NP-hard combinatorial problems, with the aim of exploring the ability of its extremal dynamics to face neutral fitness landscapes by exploiting co-option by exaptation. A comparison has been effected between Extremal Optimization and a Random Mutation Hill Climber on several problem instances of a well-known neutral fitness landscape, i.e., the Royal Road.