The probably approximately correct (PAC) population size of a genetic algorithm

Authors:
A. Hernandez-Aguirre;A. Martinez-Alcantara
Affiliations:
-;-
Venue:
ICTAI '00 Proceedings of the 12th IEEE International Conference on Tools with Artificial Intelligence
Year:
2000

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Abstract

Abstract: Probably approximately correct learning, PAC-learning, is a framework for the study of learnability and learning machines. In this framework, learning is induced through a set of examples. The size of this set is such that with probability greater than 1-/spl delta/ the learning machine shows an approximately correct behavior with error no greater than /spl epsiv/. The authors use the PAC framework to derive the size of a GA population that with probability 1-/spl delta/ contains at least one individual /spl epsiv/-close to a target hypothesis or solution.