Transmutations of Images – By Genetic Algorithms

Genetic algorithm (GA) is the type of Soft Computing or Artificial Intelligence method. The GA is a representation of machine learning which grows its behaviour from an image of the processes of evolution in nature. The objective here is to improve the quality of the image, mutating the image to other image and to convert the image into segments to get more meaningful image and it will be easy to analyze the image using genetic algorithm. In order to use a genetic algorithm to solve a problem, a function is needed of converting a string of numbers (or symbols) into possible solutions to the problem. This permits solutions to be mutated by changing symbols at random or by changing the elements of three-dimensional array representing RGB pixels. Mainly, every possible string of symbols must generate a valid solution. It is also required a way of measuring the fitness of a solution -how close is it to solving the problem from tip to toe. Comparison of two potential solutions is allowed to select the better solution. Genetic algorithm is the fair-minded optimization technique. It is useful in image mutation, enhancement and segmentation. GA is proven in many research works as the most powerful optimization technique in a large solution space. This is the reason for increasing popularity of GAs applications in image processing and other fields. A simple genetic algorithm works in the following way:

Produce a random genome (the mother's genome)

This paper gives an analytical overview of the genetic algorithm to review the tasks of image processing with mutation through RGB pixels, enhancement & segmentation of images.