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Microstrip Antennas Part 9

Tham khảo tài liệu 'microstrip antennas part 9', kỹ thuật - công nghệ, cơ khí - chế tạo máy phục vụ nhu cầu học tập, nghiên cứu và làm việc hiệu quả | Methods to Design Microstrip Antennas for Modern Applications 229 Step 5. Using potentially a threshold value for each component of each cost vector or for the total of them we select a subtotal of the Ni chromosomes which will be the population of the next generation. The thresholds may be not constant and the new generation includes the individuals that correspond to all the gci p1 which passed the test of thresholding and at the same time ensure the feasibility of the antenna. In this way the number chromosomes in the populations gradually decrease and better performers are included from generation to generation. Step 6. The process goes back to step 3 for the mating of the members of the new generation. This procedure is repeated iteratively either a pre-specified number of times or until all the chromosomes of the population fulfill a pre-defined criterion. Additional steps as the mutation which is a separate process to change the chromosomes or and elitism strategies can be incorporated in the GA depending on the problem. Elitism strategies give solution to the following problem during the process of a simple genetic algorithm it is possible for the next generation to have a best individual with a lower fitness with concern to the cost function than the best individual encountered in a preceding generation. This loss of the best individual occurs due to the probabilistic nature of the GA selection and mutation. A simple test can be added to verify that the best individual in the new generation is at least as good as the one from the preceding generation. Saving and inserting the best individuals from the last generation is known as an elitist action. Elitism can be used to ensure that there is a monotonic increase in the best fitness in the population as a function of time during the GA process. The basic characteristics and the advantages of the GAs are summarized as follows 1. The GAs can be used in problems with one or more objective functions. In these .