An adaptive differential evolution algorithm
The performance of Differential Evolution (DE) algorithm is significantly affected by its parameter setting. But the choice of parameters is heavily dependent on the problem characteristics. Therefore, recently a couple of adaptation schemes that automatically adjust DE parameters have been proposed. The current work presents another adaptation scheme for DE parameters namely amplification factor and crossover rate. We systematically analyze the effectiveness of the proposed adaptation scheme for DE parameters using a standard benchmark suite consisting of ten functions. The undertaken empirical study shows that the proposed adaptive DE (aDE) algorithm exhibits an overall better performance compared to other prominent adaptive DE algorithms as well as canonical DE.
As environments surrounding the Internet become more changeable, a design approach is needed that requires less equipment to scale up networks against traffic growth resulting from various environmental changes. Here, we propose an evolvable network design approach in which network equipment is deployed without predetermined purpose rather than for a preplanned purpose. We use mutual information on node degree to measure the topological diversity of networks, and maximize topological diversity in the network design by minimizing the mutual information. Evaluations show that, compared to networks based on an ad hoc design method, networks constructed by our design approach can re-use already installed equipment in different environments.