Computer Science – Neural and Evolutionary Computing
Scientific paper
2011-03-29
Computer Science
Neural and Evolutionary Computing
12 pages
Scientific paper
Genetic Programming (GP) has found various applications. Understanding this type of algorithm from a theoretical point of view is a challenging task. The first results on the computational complexity of GP have been obtained for problems with isolated program semantics. With this paper, we push forward the computational complexity analysis of GP on a problem with dependent program semantics. We study the well-known sorting problem in this context and analyze rigorously how GP can deal with different measures of sortedness.
Neumann Frank
Wagner Markus
No associations
LandOfFree
Computational Complexity Results for Genetic Programming and the Sorting Problem does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Computational Complexity Results for Genetic Programming and the Sorting Problem, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Computational Complexity Results for Genetic Programming and the Sorting Problem will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-566953