An Improved Genetic Algorithm Based on the subdivision Theory for Function Optimization

In this paper an improved genetic algorithm is proposed to solve optimal problems applying fixed point algorithms of continuous self-mapping in Euclidean space. The algorithm operates on subdivision of searching space and generates the integer labels at the vertices, and then only mutation operator relying on the genetic encoding designed which is proposed by virtue of the concept of relative coordinates. In this case, whether every individual of the population is in a completely labeled simplex can be used as an objective convergence criterion and determined whether the algorithm will be terminated. The algorithm combines genetic algorithms with fixed point algorithms and gradually fine mesh. Finally, an example is provided to be examined which demonstrate the effectiveness of this method.