We are not able to resolve this OAI Identifier to the repository landing page. If you are the repository manager for this record, please head to the Dashboard and adjust the settings.
This article studies the sub-tree operators: mutation and crossover, within
the context of Genetic Programming. Two standard problems, symbolic linear
regression and a non-linear tree, were presented to the algorithm at each stage.
The behaviour of the operators in regard to fitness is first established, followed
by an analysis of the most optimal ratio between crossover and mutation.
Subsequently, three algorithms are presented as candidates to dynamically
learn the most optimal level of this ratio. The results of each algorithm are
then compared to each other and the traditional constant ratio
Is data on this page outdated, violates copyrights or anything else? Report the problem now and we will take corresponding actions after reviewing your request.