A robust evolutionary algorithm for global optimization
Resource
Engineering Optimization, vol.34(5), pp.405-427, 2002
Journal
Engineering Optimization
Journal Volume
34
Journal Issue
5
Pages
405-427
Date Issued
2003
Date
2003
Author(s)
Abstract
This paper studies an evolutionary algorithm for global optimization. Based on family competition and adaptive rules, the proposed approach consists of global and local strategies by integrating decreasing-based mutations and self-adaptive mutations. The proposed approach is experimentally analyzed by showing that its components can integrate with one another and possess good local and global properties. Following the description of implementation details, the approach is then applied to several widely used test sets, including problems from international contests on evolutionary optimization. Numerical results indicate that the new approach performs very robustly and is competitive with other well-known evolutionary algorithms.
Subjects
Adaptive rules; Evolutionary algorithms; Family competition; Global optimization; Multiple mutation operators
Other Subjects
Evolutionary algorithms; Functions; Mathematical operators; Problem solving; Set theory; Adaptive rules; Family competition; Multiple mutation operators; Global optimization
Type
journal article