A Genetic Algorithm Approach Towards Image Optimization

In today’s world, an optimal and intelligent problem solving approaches are required in every field, regardless of simple or complex problems. Researches and developers are trying to make machines and software's more efficient and intelligent. This is where the Artificial Intelligence plays its role in developing efficient and optimal searching algorithm solutions. Genetic algorithm is one of most pervasive and advanced developed heuristic search technique in AI. Genetic algorithm (GA) is developed to find the most optimized solution for a given problem based on inheritance, mutation, selection and some other techniques. GA has proved itself to be a powerful, unbiased optimization technique in today’s industry. It was proved that genetic algorithms are the most powerful unbiased optimization techniques for sampling a large solution space. They were applied for the image enhancement, segmentation, feature extraction and classification as well as the image. In this paper, we have included the genetic algorithm flowchart with basic parameters that include the crossover probability, number of cross-over points, mutation probability, maximum number of iterations and population size. A case study on how images can be reproduced using the optimal parameters is conducted. The image used was the SUTS logo and it was reproduced using the GA.