How to Manage Our Budget Efficiently

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1. INTRODUCTION

The project illustrated here is related to our day to day life where we have to purchase things and we have constraints with us i.e. with in given constraints of money we have to manage our budget efficiently.

The project includes of a buyer , who has to invest his money for purchasing items. Every item is associated with a price and also has a margin. The margin indicates the profit which the buyer gets when he sells that item.

This is quite similar to knapsack problem where we have weight of knapsack and items with weights and their associated profit value. By relating this application with knapsack, here weight of bag is considered as total money that the buyer has and the items weights related to item price and the value in knapsack is related to the profit margin .

The knapsack problem is NP problem which means that it can’t be solved in polynomial amount of time , so the project uses Genetic Algorithm to implement this.

There are some real life examples where we can implement this application for e.g.
• In a stationary shop we have many stationary items like pen, pencils, notebooks and many others. So how a shopkeeper should decide to keep items in the shop which will give him maximum profit when he have limited money to purchase things.
• In canteen or any other food place there are many items like burgers, sandwiches, cold drinks and so on , so how one select items with in limited budget can be done with this application.

For future work with the help of self learning techniques we will make the application better. Learning will give some better results and most appropriate solutions.

2. LITRATURE SURVEY

2.1 0/1 KNAPSACK PROBLEM

The 0/1 knapsack problem is a problem in combi...

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...e local optima .A few researchers used diversity measure to control the search direction of evolutionary algorithms.

By means of mixing adaptive crossover and mutation with diversity-guided mutation and modifying adaptive crossover strategies ,an adaptive genetic algorithm with diversity-guided mutation(MHAGA)was developed[8].It is proved that AGADM will converge to the global optimum ,but(AGA)do not always do so . AGADM to solve 0-1 knapsack problem, and use the greedy transform algorithm to repair infeasible solution and the problem of insufficient backpack resource utilization. therefore, AGADM based on greedy algorithm(named Modified Hybrid Adaptive Genetic Algorithm, MHAGA).
2.3.5 MODIFIED ADAPTIVE OPERATORS
Crossover and mutation are the key which affects behavior and performance of GA. Adaptive crossover and mutation probabilities of individuals (denoted

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