By Topic

Evolving search spaces to emphasize the performance difference of real-coded crossovers using genetic programming

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)
Shirakawa, S. ; Grad. Sch. of Environ. & Inf. Sci., Yokohama Nat. Univ., Yokohama, Japan ; Yata, N. ; Nagao, T.

When we evaluate the search performance of an evolutionary computation (EC) technique, we usually apply it to typical benchmark functions and evaluate its performance in comparison to other techniques. In experiments on limited benchmark functions, it can be difficult to understand the features of each technique. In this paper, the search spaces that emphasize the performance difference of EC techniques are evolved by Cartesian genetic programming. We focus on a real-coded genetic algorithm, which is a type of genetic algorithm that has a real-valued vector as a chromosome. In particular, we generate search spaces using the performance difference of real-coded crossovers. In the experiments, we evolve the search spaces using the combination of three types of real-coded crossovers. As a result of our experiments, the search spaces that exhibit the largest performance difference of two crossovers are generated for all the combinations.

Published in:

Evolutionary Computation (CEC), 2010 IEEE Congress on

Date of Conference:

18-23 July 2010