Abstract:In this paper, an attractive approach for teaching genetic algorithm (GA) is presented. This approach is based primarily on using MATLAB in implementing the genetic operators: initialization, crossover, mutation, evaluation and selection. A detailed illustrative examples is presented to demonstrate that how to solve Traveling Salesman Problem (TSP) and Drawing the largest possible circle in a space of stars without enclosing any of them.

Abstract:In this paper, a variant of the Genetic Algorithm is used to place sensors optimally on a Large Space Structure for the purpose of modal identification. The selection and reproduction schemes of the Genetic Algorithm are modified and a new operator called forced mutation is introduced. These changes are shown to improve the convergence of the algorithm and to lead to near optimal sensor locations. genetic programming combined with neural networks could be incredibly slow, thus impractical. As with many problems, you have to constrain what you are attempting to create.