Abstract
This study proposes a modified
version of cultural algorithms (CAs) which benefits
from rule-based system for influence function. This
rule-based system selects and applies the suitable
knowledge source according to the distribution of
the solutions. This is important to use appropriate
influence function to apply to a specific individual,
regarding to its role in the search process. This
rule based system is optimized using Genetic
Algorithm (GA). The proposed modified CA
algorithm is compared with several other
optimization algorithms including GA, particle
swarm optimization (PSO), especially standard
version of cultural algorithm. The obtained results
demonstrate that the proposed modification
enhances the performance of the CA in terms of
global optimality.
version of cultural algorithms (CAs) which benefits
from rule-based system for influence function. This
rule-based system selects and applies the suitable
knowledge source according to the distribution of
the solutions. This is important to use appropriate
influence function to apply to a specific individual,
regarding to its role in the search process. This
rule based system is optimized using Genetic
Algorithm (GA). The proposed modified CA
algorithm is compared with several other
optimization algorithms including GA, particle
swarm optimization (PSO), especially standard
version of cultural algorithm. The obtained results
demonstrate that the proposed modification
enhances the performance of the CA in terms of
global optimality.
| Original language | English |
|---|---|
| Pages (from-to) | 29-38 |
| Number of pages | 10 |
| Journal | Journal of Advances in Computer Engineering and Technology |
| Volume | 1 |
| Issue number | 2 |
| Publication status | Published - 1 May 2015 |
| Externally published | Yes |