A comparative study of modified crossover operators

Genetic Algorithms (GA) are based on natural evolution theory called `Darwin´s Theory of Evolution´. In the area of optimization and search problems the genetic algorithm can work efficiently and give better results. This paper presents traditional single point, two point and uniform crossover operators with cyclic technique to solve the Travelling Salesman Problem (TSP). The three different proposed crossover operators are applied on the TSP. The experimental result shows that the genetic algorithm with crossover operators gives better results with low mutation rate. It also compares the performance of these new single point, two point and uniform crossover operators on different population sizes and concludes that crossover operator works efficiently when population size is large. When these modified crossover operators are compared, the results shows that the modified single point crossover operator gives better result.