By Topic

Evolutionary programming using mutations based on the Levy probability distribution

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
$31 $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

2 Author(s)
Chang-Yong Lee ; Dept. of Ind. Inf., Kongju Nat. Univ., Chungnam, South Korea ; Xin Yao

Studies evolutionary programming with mutations based on the Levy probability distribution. The Levy probability distribution has an infinite second moment and is, therefore, more likely to generate an offspring that is farther away from its parent than the commonly employed Gaussian mutation. Such likelihood depends on a parameter α in the Levy distribution. We propose an evolutionary programming algorithm using adaptive as well as nonadaptive Levy mutations. The proposed algorithm was applied to multivariate functional optimization. Empirical evidence shows that, in the case of functions having many local optima, the performance of the proposed algorithm was better than that of classical evolutionary programming using Gaussian mutation.

Published in:

Evolutionary Computation, IEEE Transactions on  (Volume:8 ,  Issue: 1 )