On the Computational Power of Adaptive Systems

Authors

  • C. Cotta
  • E. Alba
  • J. M. Troya

Abstract

Recent research has demonstrated that no search algorithm is better than any other one when performance is averaged over all possible discrete problems. Hybridization (incorporation of problem-knowledge) is required to produce adequate problem-specific algorithms. This work explores the power of hybridization in the context of evolutionary algorithms. For this purpose, a framework for describing adaptive systems is presented. It is shown that, when hybridized, adaptive techniques are computationally complete systems with Turing capabilities. Moreover, evolutionary algorithms can be regarded as a kind of nondeterministic Turing machines.

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Published

2012-03-01

How to Cite

Cotta, C., Alba, E., & Troya, J. M. (2012). On the Computational Power of Adaptive Systems. Computing and Informatics, 19(1), 3–18. Retrieved from http://147.213.75.17/ojs/index.php/cai/article/view/550