This paper presents a powerful genetic algorithm(GA) to solve the traveling
salesman problem (TSP). To construct a powerful GA, I use edge swapping(ES)
with a local search procedure to determine good combinations of building blocks
of parent solutions for generating even better offspring solutions.
Experimental results on well studied TSP benchmarks demonstrate that the
proposed GA is competitive in finding very high quality solutions on instances
with up to 16,862 cities.