New Approach for Solving Discrete Problems With Huge Amount Of Variables Through Optimizing Current Genetic Algorithm Crossover Techniques

A genetic algorithm has been used to search for artificial intelligence and computing discipline. It assists researchers in finding the most optimized solutions to search problems based on the theory of natural selection and evolutionary biology. Genetic algorithms are robust search algorithm which is appropriate for search in large and complex data sets. There are many ways to produce the individuals in GA through using the crossover and mutation techniques. The final vision of any GA is to maximize fitness function. This paper has proposed a new technique for genetic algorithm uniform crossover by optimizing previous methods. Proposed techniques are developed and tested in the MATLAB platform to see and evaluate the gained result with current crossover techniques. The result shows meaningful improvement in terms of reducing the number of iterations and function evaluations.