Automatic Combination of Operators in a Genetic Algorithm to Solve the Traveling Salesman Problem

Abstract

Genetic algorithms are powerful search methods inspired by Darwinian evolution. To date, they have been applied to the solution of many optimization problems because of the easy use of their properties and their robustness in finding good solutions to difficult problems. The good operation of genetic algorithms is due in part to its two main variation operators, namely, crossover and mutation operators. Typically, in the literature, we find the use of a single crossover and mutation operator. However, there are studies that have shown that using multi-operators produces synergy and that the operators are mutually complementary. Using multi-operators is not a simple task because which operators to use and how to combine them must be determined, which in itself is an optimization problem. In this paper, it is proposed that the task of exploring the different combinations of the crossover and mutation operators can be carried out by evolutionary computing. The crossover and mutation operators used are those typically used for solving the traveling salesman problem. The process of searching for good combinations was effective, yielding appropriate and synergic combinations of the crossover and mutation operators. The numerical results show that the use of the combination of operators obtained by evolutionary computing is better than the use of a single operator and the use of multi-operators combined in the standard way. The results were also better than those of the last operators reported in the literature.

An efficient parallel genetic algorithm solution for vehicle routing problem in cloud implementation of the intelligent transportation systems

“…The statistical analysis of the methods of selection in genetic algorithms, as well as other operators, is already studied in many researches including [35]. This issue has a significant effect in designing efficient GAs [36].…”

A Survey of Genetic Programming and Its Applications

“…The variation between the mutation and recombination is that the recombination applies two parents to reproduce a new child whereas the mutation only focuses on a parent and alters its genotype to form the new child. Various mutation operators are employed in the GPs recently that several will be described below [68], [69], [73], [74].…”

Trajectory planning for robotic maintenance of pasture based on approximation algorithms

“…Different optimisation techniques have been proposed, for instance genetic algorithms, simulated annealing, the Concorde TSP Colver, particle swarm optimisations and ant colony optimisations (see the presentation of several methods and comparative studies by Alhanjouri (2017)). Evolutionary algorithms -and particularly the genetic algorithms inspired by Darwin's theory of survival of the fittest -are powerful methods to obtain approximate solutions to the TSP (Contreras-Bolton & Parada, 2015;Potvin, 1996). Because of this, a genetic algorithm, adapted from Kirk (2007) was used in this paper.…”