This paper addresses the problem of actively control acoustic noise in ducts through the application of genetic algorithm and genetic programming (termed GENETIC CONTROL). Genetic programming is applied to structure a control strategy and genetic algorithm to adapt the resulting model parameters in real time. For application in real time situations, a gain/delay model was finally coded in the c...

We describe a genetic algorithm based heuristic for distributed database (DDB) design, Relation Clustering (RC). Relations in a database are allocated to nodes of a distributed database such that the total cost of executing a set of queries (each with a given frequency) over a time period is minimized. The experimental results are compared with another earlier GA based algorithm which shows abo...

Genetic algorithm behavior is determined by the exploration/exploitation balance kept throughout the run. When this balance is disproportionate, the premature convergence problem will probably appear, causing a drop in the genetic algorithm's eecacy. One approach presented for dealing with this problem is the distributed genetic algorithm model. Its basic idea is to keep, in parallel, several s...

In this paper, we propose an approach for designing and quantitatively assessing the performance of the multilayered radarabsorbing structure. In our proposed approach, a five layered radarabsorbing materials design is optimized from the predefined materials database. But to determine the optimal choice of the material and thickness of each layer, a combined binary and real-coded genetic algori...

microgrids are small-scale, low voltage (lv) power networks which employ renewable distribution energy resources (ders) with power electronic interfaces (peis). microgrids as single controlled units and active distribution networks require flexible control systems to ensure reliable and secure operation in different modes. these various operations of microgrid cause variations in voltage and fr...

This paper applies the Differential Evolution (DE) and Genetic Algorithm (GA) to the task of automatic fuzzy clustering in a Multi-objective Optimization (MO) framework. It compares the performance a hybrid of the GA and DE (GADE) algorithms over the fuzzy clustering problem, where two conflicting fuzzy validity indices are simultaneously optimized. The resultant Pareto optimal set of solutions...

Genetic algorithms (GAs) are vital members within the family biologically inspired algorithms. It has been proven that the performance of GAs is largely affected by the type of encoding schemes used to encode optimization problems. Binary and real encoding schemes are the most popular ones. However, it is still controversial to decide the superiority of one of them for GAs performance. Therefor...