By Topic

Improved Genetic Algorithms to Solving Constrained Optimization Problems

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

3 Author(s)
Zhu Can ; Coll. of Inf. Sci. & Eng., Central South Univ., Changsha, China ; Liang Xi-Ming ; Zhou Shu-renhu

The slow convergence speed and the lack of effective constraint handling strategies are the major concerns when applying genetic algorithms (Gas) to constrained optimization problem. An improved genetic algorithm was proposed by dividing population into three parts: optimal subpopulation, elitists subpopulation and spare subpopulation. We applied genetic algorithm on three subpopulations with different evolutionary strategies. Isolation of optimal subpopulation was to improve convergence speed. Population diversity was kept by spare subpopulation setting and aperiodically decreasing of the size of optimal subpopulation. Gene segregation was carried out by crossover operation between optimal subpopulation and spare subpopulation. Combination of penalty function method and the strategy of elitists preservation by setting elitists subpopulation was used to constraint handling. Some numerical tests have been made and the results show that the algorithm is effective.

Published in:

Computational Intelligence and Natural Computing, 2009. CINC '09. International Conference on  (Volume:1 )

Date of Conference:

6-7 June 2009