Genetic Algorithm for finding shortest paths Problem

Abstract

Genetic algorithm is used for analyzing business problems mostly applied to find solution for business challenges. Genetic algorithm generates many solutions to a single problem each one with different performance some are better than other in performance. Finding shortest path has many applications in different fields. The purpose of this paper is to find the business problem related to supermarkets and give the solution to this problem using genetic algorithm. In this paper we chose supermarkets in different location and we want to find the shortest path for the manager to visit among these supermarkets with shortest distance, therefore in genetic algorithm the evaluation function finds all the relevant variables of Geno type and then evaluates fitness function on these Geno variables using crossover and mutation techniques for sorting out relevant values. In GA the fitness function is the total time elapsed for the target to achieve their goal. We use the fitness function on population data with respect to crossover and mutation function for training datasets. The main problems in Business, science and engineering are to find the short path in different activities like visiting different places or transferring some data in minimum time. The genetic algorithm provides efficient method to provide optimizations of these problems. To solve the supermarket manger traveling problem we encode the datasets of our problem using GA and initialize all the variables. The estimation values of the locations are set as a parameter to genetic algorithm and find the best method by using the MDL technique (Minimum Description length). Genetic algorithms are very efficient for selection and genetics of natural values.