on the detection of general problem structures by using inductive linkage identification
Genetic algorithms and the descendant methods have been deemed robust and practical. To enhance the capabilities of genetic algorithms, tremendous effort has been invested in the field of evolutionary computation. One of the major trends to enhance genetic algorithms is to extract and exploit the relationship among variables, such as estimation of distribution algorithms and perturbation-based methods. In this study, we make an attempt to enable inductive linkage identification (ILI) to detect general problem structures, in which one variable may link to an arbitrary number of other variables. Our results indicate that the proposed technique can successfully detect the given problem structure.
problem structure inductive linkage identification genetic algorithm evolutionary computation perturbation-based method ili non-overlapping building block linkage learning problem decomposition overlapping building block