The goal of linkage identification is to obtain the dependencies among decision variables. Such information or knowledge can be applied to design crossover operators and/or the encoding schemes in genetic and evolutionary methods. Thus, promising sub-solutions to the problem will be disrupted less likely, and successful convergence may be achieved more likely. To obtain linkage information, a linkage identification technique, called Inductive Linkage Identification (ILI), was proposed recently. ILI was established upon the mechanism of perturbation and the idea of decision tree learning. By constructing a decision tree according to decision variables and fitness difference values, the interdependent variables will be determined by the adopted decision tree learning algorithm. In this article, we aim to acquire a better understanding on the characteristics of ILI, especially its behaviour under problems composed of different-sized and different-type building blocks (BBs) which are not overlapped. Experiments showed that ILI can efficiently handle BBs of different sizes and is insensitive to BB types. Our experimental observations indicate the flexibility and the applicability of ILI on various elementary BB types that are commonly adopted in related experiments.

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.

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