1. INTRODUCTIONThe project illustrated here is related to our daily life where we have to buy things and we have constraints with us, i.e. with certain money constraints we have to manage our budget efficiently. The project involves a buyer, who must invest his money in purchasing items. Each item has a price associated with it and also has a margin. The margin indicates the profit the buyer gets when selling that item. This is quite similar to the backpack problem where we have the weight of the backpack and the objects with the weights and their associated profit value. Relating this application with backpack, here the weight of the bag is considered as the total money owned by the buyer and the weight of the items is related to the price of the item and the value in the backpack is related to the profit margin. The knapsack problem is an NP problem, meaning it cannot be solved in a polynomial amount of time, so the project uses a genetic algorithm to implement it. There are some real life examples where we can implement this application such as • In a stationery store we have many stationery items like pens, pencils, notebooks and many more. So how should a shopkeeper decide to keep items in the shop that will give him maximum profit when he has limited money to buy things. • In the canteen or any other catering place there are many items like burgers, sandwiches, cold drinks and so on, so with this application you can select items within a limited budget. For future work with the help of self-learning techniques we will improve the application. Learning will provide better results and more appropriate solutions.2. LITRATURES SURVEY2.1 BACKPACK PROBLEM 0/1The knapsack problem 0/1 is a problem in combi...... half of the document ......and local optimum. Some researchers have used the diversity measure to control the search direction of evolutionary algorithms. By mixing adaptive crossover and diversity-driven mutation and modifying adaptive crossover strategies, a diversity-driven mutation adaptive genetic algorithm (MHAGA) was developed [8]. It is proven that AGADM will converge to the global optimum, but (AGA) does not always do so. AGADM to solve the 0-1 knapsack problem, and use the greedy transformation algorithm to fix infeasible solutions and the knapsack resource underutilization problem. therefore, AGADM based on a greedy algorithm (called Modified Hybrid Adaptive Genetic Algorithm, MHAGA). 2.3.5 MODIFIED ADAPTIVE OPERATORS Crossover and mutation are the key influencing the behavior and performance of GA. Adaptive crossover and mutation probability of individuals (denoted