Genetic algorithms mutation operator

The evolutionary process of a genetic algorithm is accomplished by genetic operators which translate the evolutionary concepts of selection, recombination or crossover, and mutation into data processing to solve an optimization problem dynamically. Possible solutions to the problem are coded as so-called artificial chromosomes, which are changed and adapted throughout the optimization process until an optimrun solution is obtained. 

Table 11.3 One pass (read left to right) through the step.s of a basic genetic algorithm scheme to maximize the fitness function f x) = using a population of six 6-bit chromosomes. The crossover notation aina2) means that chromosomes Ca, and Ca2 exchange bits beyond the bit. The underlined bits in the Mutation Operation column are the only ones that have undergone random mutation. See text for other details.

Steps 4 through 6 are the scatter search counterparts to the crossover and mutation operators in genetic algorithms, and the reference set corresponds to the GA... 

These results were obtained by coupling a genetic algorithm for descriptor and calculation parameter (PC, bins) selection to PCA-based partitioning. In these calculations, descriptors were chosen from a pool of approx 150 different ones, and both the number of PCs and bins were allowed to vary from 1 to 15. An initial population of 300 chromosomes was randomly generated with initial bit occupancy of approx 15%. Rates for mutation and crossover operations were set to 5% and 25%, respectively. After PCA-based partitioning, scores were calculated for the following fitness function ... 

A variable selection approach based on genetic algorithms, and, more specifically, on an evolutionary algorithm, mutation being the preferred genetic operation [Kubinyi, 1994a Kubinyi, 1994b Kubinyi, 1996]. 

Figure 29 The genetic operators mutation and crossover in genetic algorithms.

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