International Journal of Computational
Intelligence Research (IJCIR)
Volume 2, Number 2 (2006)
A dynamic distributed double guided genetic algorithm for optimization
and constraint reasoning
Bouamama Sadok, Ghedira Khaled
SOIE Laboratory, University of TUNIS
D3G2A is a new multi-agent approach which addresses additive Constraint Satisfaction Problems
(∑CSPs). This approach is inspired by the guided genetic algorithm (GGA) and by the Dynamic distributed double guided genetic algorithm for Max_CSPs. It consists of agents dynamically created and cooperating in order to solve problems with each agent performs its own GA. First, our approach is enhanced by many new parameters. These latter allow not only diversification but also escaping from local optima. Second, the GGAs performed agents will no longer be the same. This is stirred by NEO-DARWINISM theory and the nature laws. In fact our approach will let the agents able to count their own GA parameters. In order to show
D3G2A advantages, the approach and the GGA are applied on the radio link frequency allocation problem (RLFAP) and on the randomly generated binary CSPs.
Constraints satisfaction, genetic algorithms, guidance, NEO-DARWINISM theory..