By Topic

Multi-population Parallel Genetic Algorithm for Economic Statistical Information Mining Based on Gene Expression 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

6 Author(s)

Function discovery is an important research direction in data mining and economic statistical target forecast. Gene expression programming (GEP) is a new tool to discovery the function in economic target analysis field. To overcome the deficiency such as pre-maturity and biggish stagnancy generation in GEP, this study (1) Introduces a dynamic mutation operator ( DM-GEP ) and flexibility controlling of population scale (FC-GEP) for more faster jumping local optimum trap and shortening average convergence generation in evolution, (2) Proposes a genome diversity-guided of grading evolution strategy for stakeout and melioration of GEP evolution process, (3) implements a multi-genome child-population parallel genetic strategy and a PED- GEP algorithm for increasing average maximal fitness and success ratio, and (4) demonstrates the effectiveness and efficiency of the new algorithm by extensive experiments, Comparising with transitional GEP, the average convergence generation is decrease to 35 % at least, and average maximal fitness increases 8 %leastways.

Published in:

Third International Conference on Natural Computation (ICNC 2007)  (Volume:3 )

Date of Conference:

24-27 Aug. 2007