Adjusting Encoding in Genetic Algorithms Dynamically by Gene Weights
基于基因权重动态调整遗传算法的编码

Crossovers, in the standard Genetic Algorithm(SGA), tend to destroy excellent schemata whose defining lengths are comparatively long. Therefore, we propose the method of dynamic ranking encoding, to improve the performance of crossover. This method, firstly, determines gene weights in the chromosome for the current population, and then ranks gene loci dynamically by gene weights to make excellent genes concentrated. Thus, the shortcoming of crossovers in SGA is overcome. Moreover, we improve the mutation to avoid the remaining local optima of GAs. At last, many experiments are done, and according to the results of these experiments, we evaluate this method.