By Topic

Grouping-based evolutionary algorithm: seeking balance between feasible and infeasible individuals of 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
$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

2 Author(s)
Ming Yuchi ; Dept. of Electr. Eng. & Comput. Sci., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea ; Jong-Hwan Kim

Most of the optimization problems in the real world have constraints. In recent years, evolutionary algorithms caught a lot of researchers' attention for solving constrained optimization problems. Infeasible individuals are often underrated by most of the current evolutionary algorithms when evolutionary algorithms are used for solving constraint optimization problems. This paper proposes an approach to balance the feasible and infeasible individuals. Feasible and infeasible individuals are divided into two groups: feasible group and infeasible group. The evaluation and ranking of these two groups are performed separately. Parents for reproduction are selected from the two groups by a parent selection method. Objective function and bubble sort method are selected as the fitness function and ranking method for the feasible group. One existing evolutionary algorithm: stochastic ranking method, is modified to evaluate and rank the infeasible group. The new method is tested using a (μ, λ)-ES on 13 benchmark problems. The results show that the proposed method is capable of improving the searching performance of the stochastic ranking method.

Published in:

Evolutionary Computation, 2004. CEC2004. Congress on  (Volume:1 )

Date of Conference:

19-23 June 2004