The Role of Crossover in Genetic Algorithms to Solve Optimization of a Function Problem

Abstract

The genetic algorithm is an adaptive search method that has the ability for a smart search to find the best solution and to reduce the number of trials and time required for obtaining the optimal solution.The aim of this paper was to study the behavior of different types of crossover operators in the performance of GA. We have also studied the effects of the parameters and variables (crossover probability, mutation rate, population size and number of generation) for controls the algorithm. This work accumulated some types of crossover operators to be a reference to all researchers; it was implemented on Optimization of a function. We investigate to explore the role of crossover in GAs with respect to this problem, by using a comparison study of the iteration results obtained from change the parameters values (crossover probability, mutation rate, population size and number of generation).The experimental results reported will show more light into how crossover effects the GAs search power in the context of optimization problems.The work explains the role of crossover operators in GAs and it shows the iteration results obtained with implementation in Delphi version 6.0 visual programming language exploiting the object oriented tools of this language.