Analysis of the suitability of using blind crossover operators in genetic algorithms for solving routing problems

Abstract

Genetic algorithms (GA) are one of the most successful techniques in solving combinatorial optimization problems. Its general character has enabled its application to different types of problems: vehicle routing, planning, scheduling, etc. This article shows that there is controversy in the basic structure of the algorithm steps when it is applied at routing problems. Specifically in this paper we show that the crossover (CX) offers no advantage in the optimization process. To solve such problems, the most important steps are mutation and selection of individuals. These two steps are what help to analyze the solution space exhaustively and give GA optimization capability. To prove our hypothesis we will analyze the results obtained by applying different blind crossover operators to solve multiple instances of the TSP (Travelling Salesman Problem).