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Evolvability from learning algorithms

Conference Paper 13B Algorithms and Complexity ยท Theoretical Computer Science

Abstract

Valiant has recently introduced a framework for analyzing the capabilities and the limitations of the evolutionary process of random change guided by selection. In his framework the process of acquiring a complex functionality is viewed as a substantially restricted form of PAC learning of an unknown function from a certain set of functions. Valiant showed that classes of functions evolvable in his model are also learnable in the statistical query (SQ) model of Kearns and asked whether the converse is true.

Authors

Keywords

  • evolvability
  • pac learning
  • statistical query

Context

Venue
ACM Symposium on Theory of Computing
Archive span
1969-2025
Indexed papers
4364
Paper id
781091443492770464
v2026.09.13