TGA: a new integrated approach to evolutionary algorithms

The genetic algorithm (GA) is a well-known heuristic optimization algorithm. However, it suffers from the serious problem of premature convergence, which is caused mainly by the population diversity decreasing in evolution. The authors propose a novel algorithm, called TGA, which integrates the memory structure and search strategy of Tabu Search (TS) with GA. As such, the selection efficiency is improved and the population diversity is maintained by incorporating the regeneration operator. The traveling salesman problem is used as a benchmark to evaluate TGA and compare it with GA and TS. Experimental results show that TGA achieves better performance than GA and TS in terms of both convergence speed and solution quality