harry up please D. Mutation works by randomly selecting a bit with a certain mut
ID: 3910574 • Letter: H
Question
harry up please
Explanation / Answer
1)Answer:
There are four major steps in which Genetic Algorithm works
? Selection
? Reproduction
? Crossover
? Mutation
SELECTION
During each successive generation, a proportion of the existing population is selected to breed a new generation. Individual solutions are selected through a fitness based process, where fitter solutions (as measured by a fitness function) are typically more likely to be selected. Certain selection methods rate the fitness of each solution and preferentially select the best solutions. Other methods rate only a random sample of the population, as this process may be very time-consuming. Most functions are STOCHASTIC and designed so that a small proportion of less fit solutions are selected. This helps keep the diversity of the population large, preventing premature convergence on poor solutions. Some of the SELECTION methods are as follows: Roulette Wheel Selection It works on the concept of the game Roulette. Under this game each individual gets a slice of the wheel, but more fit ones get larger slices than less fit ones. The wheel is then spun, and whichever individual "owns" the section on which it lands each time is chosen. A form of fitness-proportionate selection in which the chance of an individual's being selected is proportional to the amount by which its fitness is greater or less than its competitors' fitness. Scaling Selection As the average fitness of the population increases, the strength of the selective pressure also increases and the fitness function becomes more discriminating. This method can be helpful in making the best selection later on when all individuals have relatively high fitness and only small differences in fitness distinguish one from another. Tournament Selection Subgroups of individuals are chosen from the larger population, and members of each subgroup compete against each other. Only one individual from each subgroup is chosen to reproduce. Rank Selection Each individual in the population is assigned a numerical rank based on fitness, and selection is based on these ranking rather than absolute differences in fitness. The advantage of this method is that it can prevent very fit individuals from gaining dominance early at the expense of less fit ones, which would reduce the population's genetic diversity and might hinder attempts to find an acceptable solution
Hierarchical Selection Individuals go through multiple rounds of selection each generation. Lower-level evaluations are faster and less discriminating, while those that survive to higher levels are evaluated more rigorously. The advantage of this method is that it reduces overall computation time by using faster, less selective evaluation to weed out the majority of individuals that show little or no promise, and only subjecting those who survive this initial test to more rigorous and more computationally expensive fitness evaluation.
REPRODUCTION
Reproduction is a process in which individual strings are copied according to their objective function values that is f. We can call this function as Fitness Function. In other way around we can think this as some measure of profit, utility or goodness that we want to maximize. Here fitness values of string signifies that Strings with a higher value of would have higher probability of contributing one or more offspring to next generation . This operator is an artificial version of natural selection. The reproduction operator may be implemented in algorithmic form in a number of ways . We have used the concept of Roulette wheel here, where each current string in the population has a roulette wheel slot size in the proportion to its fitness. Each time we require another offspring , a simple spin of the weighted roulette wheel yields the reproductive candidate. In this way, more highly fit springs have a higher number of offspring in the succeeding generation . Once a string has been selected for replica this string is then entered in to a mating pool , a tentative new population for further operator action.
CROSSOVER
After reproduction, simple crossover may proceed in two steps. First each member of the newly reproduced strings in the mating pool is mated at random. Second, each pair of strings undergoes crossing over as follows. An integer position k along the string is selected uniformly at random between 1 and the string length less one [1 , l – 1]. Two new strings are created by swapping all characters between k+1 and l inclusively. Here we crossover the selected strings using the above formulae and rest of the strings are directly inherited in the matrix.
MUTATION
Mutation adds new information in a random way to the genetic search process and ultimately helps to avoid getting trapped at local optima. Mutation in a way is the process of randomly disturbing genetic information. They operate at the bit level; when the bits are being copied from the current string to the new string, there is probability that each bit may become mutated. This probability is usually a quite small value, called as mutation probability. A coin toss mechanism is employed; if random number between zero and one is less than the mutation probability, then the bit is inverted, so that zero becomes one and one becomes zero. This helps in introducing a bit of diversity to the population by scattering the occasional points. This random scattering would result in a better optima, or even modify a part of genetic code that will be beneficial in later operations. On the other hand, it might produce a weak individual that will never be selected for further operations. Mutation value is taken as 0.01 and using the random value we change the value of the binary digit if probability allows to do so
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