Study of Some Recent Crossovers Effects on Speed and Accuracy of Genetic Algorithm, Using Symmetric Travelling Salesman Problem
This is an incremental study for researchers in optimization, focusing on comparing existing crossover methods for genetic algorithms on a classic problem.
The study implemented recent crossover operators in a genetic algorithm to solve symmetric traveling salesman problem instances, comparing their effects on speed and accuracy, but no concrete numerical results were provided in the abstract.
The Travelling Salesman Problem (TSP) is one of the most famous optimization problems. The Genetic Algorithm (GA) is one of metaheuristics that have been applied to TSP. The Crossover and mutation operators are two important elements of GA. There are many TSP solver crossover operators. In this paper, we state implementation of some recent TSP solver crossovers at first and then we use each of them in GA to solve some Symmetric TSP (STSP) instances and finally compare their effects on speed and accuracy of presented GA.