A series of advanced techniques in genetic and evolutionary computation have been proposed that analyze gene linkage to realize
competent genetic algorithms
. Although it is important to encode linked variables tightly for simple GAs, it is sometimes difficult because it requires enough knowledge of problems to be solved. In order to solve real-world problems effectively even if the knowledge is not available, we need to analyze gene linkage.
We review algorithms which have been proposed that identify linkage by applying perturbations, by building a probabilistic model of promising strings, and a recombination of the both of the above.
We also introduce a context-dependent crossover that can utilize overlapping linkage information in a sophisticated manner. By employing linkage identification techniques with context dependent crossover, we can solve practical real-world application problems that usually have complex problem structures without knowing them before optimization.