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An Introduction to Genetic Algorithms phần 3

Ví dụ như các trích dẫn trước đó với cf kích thước 3 trong B, C và F thay đổi đến 300, parenthesization cùng là tối ưu, Meyer (thông tin cá nhân) tìm thấy rằng chuyển chéo ra và phụ thuộc chỉ vào các nhà khai thác đột biến bốn đã không làm cho | Chapter 2 Genetic Algorithms in Problem Solving Apply selection crossover and mutation to the population to form a new population. In Koza s method 10 of the trees in the population chosen probabilistically in proportion to fitness are copied without modification into the new population. The remaining 90 of the new population is formed by crossovers between parents selected again probabilistically in proportion to fitness from the current population. Crossover consists of choosing a random point in each parent and exchanging the subtrees beneath those points to produce two offspring. Figure 2.3 displays one possible crossover event. Notice that in contrast to the simple GA crossover here allows the size of a program to increase or decrease. Mutation might performed by choosing a random point in a tree and replacing the subtree beneath that point by a randomly generated subtree. Koza 1992 typically does not use a mutation operator in his applications instead he uses initial populations that are presumably large enough to contain a sufficient diversity of building blocks so that crossover will be sufficient to put together a working program. Figure 2.3 An example of crossover in the genetic programming algorithm. The two parents are shown at the top of the figure the two offspring below. The crossover points are indicated by slashes in the parent trees. Steps 3 and 4 are repeated for some number of generations. It may seem difficult to believe that this procedure would ever result in a correct program the famous example of a monkey randomly hitting the keys on a typewriter and producing the works of Shakespeare comes to mind. But surprising as it might seem the GP technique has succeeded in evolving correct programs to solve a large number of simple and some not-so-simple problems in optimal control planning sequence induction symbolic regression image compression robotics and many other domains. One example described in detail in Koza 1992 is the block-stacking .