The genetic algorithm (GA) is an optimization and search technique based on the principles of genetics and natural selection. A genetic algorithm is a search method that can be used for both solving problems and modeling evolutionary systems. The concept of the proposed paper is taken from simple genetic algorithm implementation using integer arrays for storage of binary strings as a basic ingredient. The Simple genetic algorithm (SGA) evaluates a group of binary strings and it performs crossover and mutation operation, which is the most important operation of genetic algorithm. SGA is successful if the final average fitness value is more than the initial average fitness value after crossover and mutation. This proposed paper deals with varying crossover points and observing its effect on SGA. Basically, the crossover point is varied from 1 to n (where n<=2) and observe its effect on both initial and final average fitness value. The probabilities of crossover and mutation are also varied. Then the proposed paper, Analysis of effect of varying crossover points on simple genetic algorithm is compared with the simple genetic algorithm implementation using integer arrays for storage of binary strings. Experimental results show that the proposed scheme significantly improves the performance of genetic algorithm.

1. Kanchan, Pradeep, D'souza Rio, "SGA implementation using integer arrays for storage of binary strings", International Conference on Machine Learning and Computing (ICMLC), 2010.
2. Koza, John R. , "Evolving a computer program to generate random numbers using the Genetic Programming paradigm", Proceedings of the Fourth International Conference on Genetic Algorithms, 1991.
3. Khan, Fozia Hanif, Khan, Nasiruddin, Inayatulla, Syed, Nizami, Shaikh Tajuddin, "Solving ISP problem by using Genetic Algorithm", International Journal of Basic &amp; Applied Sciences IJBAS-IJENS, Vol:09 No:10.
4. Koza, John R. , "Genetic Programming: A paradigm for genetically breeding populations of computer programs to solve problems", Proceedings of the 2nd International IEEE Conference on tools for Artificial Intelligence, 1990.
5. Mitchell, Melanie, "An Introduction to Genetic Algorithms", 1998 :Prentice Hall.
6. Goldberg, David E. , "Genetic Algorithms in search, Optimization and Machine Learning", 1989 :Addison-Wesley.
7. Davis, L. , "Genetic algorithms and simulated annealing", 1987 :Pittman.
8. Holland, J. H. , "Adaptation in natural and artificial systems", 1975 :University of Michigan Press.

Binary Strings Fitness Function Crossover Mutation Crossover Probability Mutation Probability