Scheduled System Maintenance:
Some services will be unavailable Sunday, March 29th through Monday, March 30th. We apologize for the inconvenience.
By Topic

Recurring Two-Stage Evolutionary Programming: A Novel Approach for Numeric Optimization

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.

The purchase and pricing options are temporarily unavailable. Please try again later.
4 Author(s)
Alam, M.S. ; Dept. of Comput. Sci ence & Eng., Bangladesh Univ. of Eng. & Technol., Dhaka, Bangladesh ; Islam, M.M. ; Yao, X. ; Murase, K.

In the application of evolutionary algorithms (EAs) to complex problem solving, it is essential to maintain proper balance between global exploration and local exploitation to achieve a good near-optimum solution to the problem. This paper presents a recurring two-stage evolutionary programming (RTEP) to balance the explorative and exploitative features of the conventional EAs. Unlike most previous works, RTEP is based on repeated and alternated execution of two different stages, namely, the exploration and exploitation stages, each with its own mutation operator, selection strategy, and explorative/exploitative objective. Both analytical and empirical studies have been carried out to understand the necessity of repeated and alternated exploration and exploitation operations in EAs. A suite of 48 benchmark numerical optimization problems has been used in the empirical studies. The experimental results show the remarkable effectiveness of the repeated exploration and exploitation operations employed by RTEP.

Published in:

Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on  (Volume:41 ,  Issue: 5 )