Sorting Using Genetic Algorithm

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ISSN (PRINT) :2320 – 8945, Volume -1, Issue -1, 2013

78

Sorting Using Genetic Algorithm

Deepali Yankanchi, Snehal Bale & Neha

Department of Computer Engineering and Information Technology, Pune Vidhyarthi Griha’s College of Engineering and Technology, Pune, India

E-mail : [email protected], [email protected], [email protected]

Abstract – Database systems have dominated software application oriented education for past two decades. Hence performing operations on the database has to be efficient and reliable. And this is possible by using the best sorting algorithm which enhances the performance of application during data retrieval and data storage. This paper describes a very efficient and reliable sorting system. This system uses a simple set of numerical data which is to be in sorted form. The mechanism of sorting using Genetic Algorithm works best in comparison with other sorting algorithm such as, Bubble sort, Merge sort, Quick sort, etc.

Genetic algorithms are different from other heuristic methods in several ways. The most important difference is that a GA works on a population of possible solutions, while other heuristic methods use a single solution in their iterations. Another difference is that GA’s are probabilistic and not deterministic.

Keywords – GA, Selection, Crossover, Mutation, Crossover probability, Mutation Probability.

I. INTRODUCTION

Sorting is a functionality required in variety of applications in different manner. Many applications need data to be in sorted form. This may gives great opportunities for creating sorting algorithms. Many sorting algorithm are used as per the application and their complexities and characteristics. Some of the sorting algorithms are: Simple Bubble, Selection, Insertion, and Merge sort, Divide & Conquer, Quick and Radix Sort.

Every Sorting algorithm is tested by their characteristics based on size, complexity of program, performance in worst & best cases, memory & speed requirements. These entire sorting algorithms have better performance in worst & best cases while considering only the small number of units of data is sorting.

In some cases, where application have the data in large scale then these sorting algorithms are not efficient

and also time consuming processes. This raises the need to have some complex algorithm which will work efficient on large amount of data. And this can be implemented using a Genetic Algorithm.

Genetic Algorithms (GAs) are adaptive heuristic search algorithm premised on the evolutionary ideas of natural selection and genetic.

The basic concept of GAs is designed to simulate processes in natural system necessary for evolution, specifically those that follow the principles first laid down by Charles Darwin of survival of the fittest. As such they represent an intelligent exploitation of a random search within a defined search space to solve a problem.

II. OPERATORS OF GENETIC ALGORITHM FITNESS FUNCTION :

The key concept within genetic algorithms is the fitness function. A fitness function merely determines the value or fitness of a (already generated) solution. It does not generate a solution itself.

Because of the nature of a genetic algorithm it is easier to talk about them solving optimization problems which search for maximums. However, this does not prevent us from using GA’s to find minimal values. A minimization problem can always be mapped mathematically into a maximization problem. To understand this we will first talk about a crucial component of all GA’s - the fitness function (also called the objective function). The fitness or objective function is used to map the individual’s bit strings into a positive number which is called the individual’s fitness. There are two steps involved in this mapping (however in some problems these two steps are essentially accomplished as one). The first step we will call

"decoding" and the second, "calculating fitness".

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ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE)

79 The life cycle of Genetic algorithm works with the main important steps:

Fig.1.0 : The flow of the steps of the algorithm.

Fig. 1.1: GA Life Cycle

The cycle explained above shows the working of Genetic algorithm.

The system which developed with Genetic Algorithm, consist of some important steps which are the main methods of Genetic Algorithm:

1. Reproduction 2. Selection 3. Crossover 4. Mutation

1. REPRODUCTION

Reproduction is usually first operator applied on population. From the population the chromosomes are selected to be parents to crossover and produce offspring.

Many reproduction operators exist and they are essentially doing something. They pick from current population the strings of above average and insert their multiple copies in the mating pool in a probabilistic manner.

2. SELECTION

Select two (parent) solutions from the original (or previous) population according to their fitness - the better fitness, the bigger chance to be selected. Most selection functions are stochastic and designed so that a small proportion of less fit solutions are selected. This helps keep the diversity of the population large, preventing premature convergence on poor solutions.

3. CROSSOVER

Cross-over is a genetic operator which is used to create two new individual solutions from two parent solutions These two parent solutions can be

‘crossovered’ (thus generating two new child solutions) in several ways. Within all these strategies, a cross-over point is selected. This cross-over point is a location within the string representation of the solutions.

4. MUTATION

After a crossover is performed, mutation takes place. Mutation is genetic operator used to perform genetic diversity from one generation of population of chromosomes to the next.

Mutation alters one or more gene values in a chromosome from its initial state. This can result in entirely new gene values being added to new gene pool.

With a new gene value, genetic algorithm may be able to arrive at better solution than was previously possible.

Mutation is an important part of genetic search, helps to prevent the population from stagnating at any local optima. Mutation is intended to prevent the search falling into local optimum of the state space

III. MATHEMATICAL MODEL

Let S be the set of stages with the help of which data is sorted.

S = {I,R,M,C,O}

Where – I= Large amount of numbers as a input data.

R = Reproduction state, M = Mutation State, C = Crossover State, O= Output in sorted form.

IV. CONCLUSION

This paper believes that, in case of large data to be used in different applications, we cannot work with these simple sorting techniques. A reliable technique which works best with huge amount of data has to be

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ITSI Transactions on Electrical and Electronics Engineering (ITSI-TEEE)

80 used. And Genetic algorithm is the best solution for that.

We have developed a sorting system with better efficiency and reliability. The paper believes that the system works with almost immediate feedback. We have implemented sorting technique which will help the user to sort large amount of data in a short period of time.

The paper describes a number of implementation features together with security aspect and some implementation details. As a conclusion we believe this kind of sorting technique to have a very high potential in the near future. Main reasons for this are that it is based on standard tools and thus can utilize advance in other information technology area and by using this software there is remarkable change in sorting techniques.

This technique is based on Genetic Algorithm. This system will save manpower, time stationary. Hence it can be used in many commercial applications.

V. FUTURE WORK

We must first increase efficiency of the system. We must also develop the system for sorting other types of data also such as large records with alphabets and large files with different format as our system mainly works with numeric data. We must develop a systematic method for finding different data within less time, for processing of the data. So all sort of information can be sorted with the help of this system.

VI. ACKNOWLEDGEMENT

The authors thank Pune Vidyarthi Griha’s College of Engineering and Technology for their support to make this research possible.

VII. REFERENCES

[1] J.P. Cohoon, S.U. Hedge, W.N. Martin, and D.

Richards. Punctuated equilibria: A parallel genetic algorithm. In J.J. Grefenstette, editor, Proceedings of the Second International Conference on Genetic Algorithms, pages 148- 154. Morgan-Kaufman, 1987.

[2] Miller, B. L. and Shaw, M. J. (1996). Genetic algorithms with dynamic niche sharing for multimodal function optimization. In IEEE International Conference on Evolutionary Computation.

[3] C. Emmanouilidis, A. Hunter and J. MacIntyre,

\A multiobjective evolutionary setting for feature selection and a commonality-based crossover operator," Proc. Congress on Evolutionary Computation, Vol. 1, 2000, pp. 309{316.

[4] N.J. Radcliffe and P. Surry (1995) Formae and the variance of fittness. In D. Whitley and M.

Vose (eds.), Foundations of Genetic Algorithms 3. Morgan Kaufmann, San Mateo, CA, pp. 51–

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