Quantitative analysis of separate and combined performance of local searcher and genetic algorithm

In this paper an environment is established for a quantitative analysis of separate and combined performance of local searchers and standard genetic algorithm. Well researched and controlled Euclidean Travelling Salesman Problem examines the impact of grafting a 2-opt based local searcher into the standard genetic algorithm for solving the Travelling Salesman Problem with Euclidean distance. Standard genetic algorithms are known to be rather slow, while 2-opt search applied to the Travelling Salesman Problem quickly gives results that are far from optimal. We propose a strategy to graft a 2-opt local searcher into a genetic algorithm, after recombination, to optimize each offspring's genomes. Genetic algorithm provides new search areas, while 2-opt improves convergence. We tested our algorithm on examples from TSPLIB and proved that this method combines good qualities from both methods applied, significantly outperforming each of them.