The Improvement And Application Research Of Dual Population Genetic Algorithm

At first, this paper summarizes the main direction of improvement of genetic algorithm , and concludes and analyzes the general dual-population genetic algorithm for the basic structure and characteristics, based on this proposes the dual-population genetic algorithm which joined the competition mechanism (CDPGA): Join the competition mechanism in the parallel operation of two relatively independent of the population to accelerate the emergence of a new individual by fierce competition between the populations, this maintains the population diversity and avoids the algorithm into the plight of precocious. At the same time, discusses two parameters (population size and life competition points) involved in CDPGA.Finally, chooses some classic functions to test the improved algorithm. Experimental results show that: compared with the general dual-population genetic algorithm, to some extent, the accuracy of the optimal solution and the search for optimal solutions are certain to more advantage in the improved algorithm. In order to allow it more suitable for solving 0/1 knapsack problem, the paper adds greedy repair operators in CDPGA, and uses it into solving three different sizes knapsack problems. The experimental results further approves the superiority of CDPGA, provide a new method to solve similar combinatorial optimization problems.