A GENETIC ALGORITHM ANALYSIS TOWARDS OPTIMIZATION SOLUTIONS

Abstract

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. It was proved that genetic algorithms are the most powerful unbiased optimization techniques for sampling a large solution space. In this paper, we have used GA for the image optimization and Knapsack Problems, which are commonly found in a real world scenario. Furthermore, a research based on a tool that uses Genetic Algorithm, called the GA Playground is done to demonstrate the capability of solving the Knapsack Problem with the fitness function and a case study on how images can be reproduced using the optimal parameters. Lastly, a few methods such as the Hash Table and the Taguchi Method are suggested to improve the performance of the Genetic Algorithm.