An Efficient Greedy Genetic Algorithm for Combination Optimization Problems
Ying Wei · Mechanical Science and Technology · 2005
Genetic algorithms often suffer from the shortcomings of slow convergence and enclosure competition. A novel greedy genetic algorithm(GGA) is proposed for combination optimization problems. Based on greedy policies, the procedure of population initialization, crossover and mutation operator produce a fitter child, for it sufficiently utilizes the local information of individuals. In the process of evolution, new individuals immigrate to the population every a few generations. All these procedures are designed to prevent premature convergence and refine the performance of genetic algorithm(GA). The simulation results of TSP show its excellent efficiency, especially during the early generation of GGA. Initial experiments demonstrated the basic promise of the approach. This work shows how GGA and GA can be usefully combined, thus pointing to a new and promising approach to combination optimization problems.