mutation operation is the random mutation of genetic material. To perform mutation in the computer, first choose a mutation probability pm. With probability Pm, change(mutate)each gene location on each chromosome randomly to a member of the number system being used. For instance, in a base-2 genetic algorithm, we could mutate 1010111to 1011111 where the fourth bit was mutated to one For a base-10 number system you would simply pick a number at random to replace a digit with if you are going to mutate a digit location Besides the fact that this helps to model mutation in a biological system, why should the genetic algorithm perform mutation? Basically, it provides random excursions into new parts of the search space. It is possible that we will get lucky and mutate to a good solution. It is the mechanism that tries to make sure that we do not get stuck at a local maxima and that we seek to explore other areas of the search space to help find a global maximum for /(9). Usually, the mutation probability is chosen to be quite small(e.g, less than 0.01)since this will help guarantee that all the individuals he mating pool are not mutated so that any search progress that was made is lost (i.e, we keep it relatively low to avoid degradation to exhaustive search via a random walk in the search space) After mutation we get a modified mating pool at time k, M(k). To form the next generation for the population, we let above steps repeat, successive generations are produced, and we thereby model evolution(of course it is a very crude a P(k+1FMk), where this M(k) is the one that was formed by selection and modified by crossover and mutation. Then th mod Optional features There have been many different options used in the definition of a genetic algorithm. These include the following There is the possibility of using other genetic operators. For instance, there is an operation called"elitism"where the most fit individual in P(k) is copied directly to P(k+/)without being changed by the other operations. This operator is sometimes used to try to make sure that there will be a reasonably fit individual present in the population at every time step; it helps to avoid having all the strings get modified by crossover and mutation in a way so that no good solution exists at some time k Some use a"population splitting"approach where the population of S members is partitioned into subsets and the genetic operations are constrained to only mix within these partitions. This can produce different subpopulations that will seek different solution There are many options for the crossover operation. For instance, some consider crossing over at every site in the chromosome. Others will perform crossover for each separate parameter(trait)on the chromosome Some will grow and shrink the population There are many other options besides the ones listed above The interested reader should consult Section 4.8, For Further Study, for more details about genetic algorithm Termination Conditions, Initialization, and Implementation Issues The above discussion showed how to produce successive generations and thereby simulate evolution. While the biological evolutionary process continues, perhaps indefinitely, there are many times when we would like to terminate out artificial one and find the following The population individual-say, -that best maximizes the fitness function. Notice that to determine this we also need to know the generation number k where the most fit individual existed (it is not necessarily in the last generation) You may want to design the computer code that implements the genetic algorithm to always keep track of the highest J value and the generation number and individual that achieved this value of J PDF文件使用" pdffactory Pro"试用版本创建ww. fineprint,com,cnmutation operation is the random mutation of genetic material. To perform mutation in the computer, first choose a mutation probability pm. With probability pm, change (mutate) each gene location on each chromosome randomly to a member of the number system being used. For instance, in a base-2 genetic algorithm, we could mutate 1010111 to 1011111, where the fourth bit was mutated to one. For a base-10 number system you would simply pick a number at random to replace a digit with if you are going to mutate a digit location. Besides the fact that this helps to model mutation in a biological system, why should the genetic algorithm perform mutation? Basically, it provides random excursions into new parts of the search space. It is possible that we will get lucky and mutate to a good solution. It is the mechanism that tries to make sure that we do not get stuck at a local maxima and that we seek to explore other areas of the search space to help find a global maximum for J (9). Usually, the mutation probability is chosen to be quite small (e.g., less than 0.01) since this will help guarantee that all the individuals in the mating pool are not mutated so that any search progress that was made is lost (i.e., we keep it relatively low to avoid degradation to exhaustive search via a random walk in the search space). After mutation we get a modified mating pool at time k, M(k). To form the next generation for the population, we let P(k+1)= M(k),where this M(k) is the one that was formed by selection and modified by crossover and mutation. Then the above steps repeat, successive generations are produced, and we thereby model evolution (of course it is a very crude model). Optional Features There have been many different options used in the definition of a genetic algorithm. These include the following: • There is the possibility of using other genetic operators. For instance, there is an operation called "elitism" where the most fit individual in P(k) is copied directly to P(k+1) without being changed by the other operations. This operator is sometimes used to try to make sure that there will be a reasonably fit individual present in the population at every time step; it helps to avoid having all the strings get modified by crossover and mutation in a way so that no good solution exists at some time k. • Some use a "population splitting" approach where the population of S members is partitioned into subsets and the genetic operations are constrained to only mix within these partitions. This can produce different subpopulations that will seek different solutions. • There are many options for the crossover operation. For instance, some consider crossing over at every site in the chromosome. Others will perform crossover for each separate parameter (trait) on the chromosome. • Some will grow and shrink the population. There are many other options besides the ones listed above. The interested reader should consult Section 4.8, For Further Study, for more details about genetic algorithms. Termination Conditions, Initialization, and Implementation Issues The above discussion showed how to produce successive generations and thereby simulate evolution. While the biological evolutionary process continues, perhaps indefinitely, there are many times when we would like to terminate out artificial one and find the following: • The population individual—say, —that best maximizes the fitness function. Notice that to determine this we also need to know the generation number k where the most fit individual existed (it is not necessarily in the last generation). You may want to design the computer code that implements the genetic algorithm to always keep track of the highest J value and the generation number and individual that achieved this value of J. PDF 文件使用 "pdfFactory Pro" 试用版本创建 www.fineprint.com.cn