Genetic Algorithm: A Veritable Tool for Solving Agricultural Extension Agents Travelling Problem

Abstract

Genetic algorithm simulates the logic of Darwinian selection as observed in the biological evolutionary process (Cells’ division, DNA, Mutation, etc) to solve problems. They are based on one hand on a heuristic gradient ascension method (selection and crossover) and in another hand on a semi-random exploration method (Mutation). In this research work, application of genetic algorithms was explored for the optimization problem embodied in the transit problem of agricultural extension agents or workers in disseminating new innovation and technological advancement in agriculture. An order representation for the cost matrix for 10 cities and chromosomes was used. The result revealed that genetic algorithm can solve the routing problem of an agricultural extension agents in terms of time minimization in order to search for the shortest route, which will increase number of places that the extension agents can touch at reduced cost of transportation. This will help in achieving the nations’ vision 2020 on food security. Keywords: Genetic algorithm, crossover, mutation, chromosome and selection.