Modification of Evolutionary Algorithm Using Wavelet Transformation

Genetic algorithm (GA) is a traditional, smart biological algorithm that has high global optimization capacity and is developed in accordance with the genomic evolution of humans in ecology. By altering its operators such as mutation, crossover, and selection, this common evolving approach may be deployed to many restricted and unbounded optimization issues as well as real-time challenges. The scaling factor of the mutation operator is altered in this article by employing the amplitude notation of wavelet transformation in GA. This change accelerates computational efficiency. The improved plan is put through its paces on a variety of standard challenges. In addition, the assessed findings are compared to the outcomes of different optimization algorithms.