By Topic

Predicting the Tide with Genetic Programming and Semantic-based Crossovers

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

3 Author(s)
Nguyen Quang Uy ; Natural Comput. Res. & Applic. Group, Univ. Coll., Dublin, Ireland ; Michael O'Neill ; Nguyen Xuan Hoai

This paper proposes an improvement of a recently proposed semantic-based crossover, Semantic Similarity-based Crossover (SSC). The new crossover, called the Most Semantic Similarity-based Crossover (MSSC), is tested with Genetic Programming (GP) on a real world problem, as in predicting the tide in Venice Lagoon, Italy. The results are compared with GP using Standard Crossover (SC) and GP using validation sets. The comparative results show that while using validation sets give only limited effect, using semantic-based crossovers, especially MSSC, remarkably improve the ability of GP to predict time series for the tested problem. Further analysis on GP code bloat helps to explain the reason behind this superiority of MSSC.

Published in:

Knowledge and Systems Engineering (KSE), 2010 Second International Conference on

Date of Conference:

7-9 Oct. 2010