This paper reviews and summarizes existing linkage learning techniques for genetic and
evolutionary algorithms in the literature. It first introduces the definition of linkage in both …

Linkage learning is employed by many state-of-the-art evolutionary methods designed for
solving problems in discrete domains. The effectiveness of these methods is dependent on …

The knowledge about variable interactions is frequently employed in state-of-the-art
research concerning Genetic Algorithms (GA). Whether these interactions are known a priori …

In this paper, we consider an NP-hard, real-world optimization problem from the field of
computer networks. The problem refers to the network survivability and may be considered …

It is known that the performance of a genetic algorithm depends on the survival environment
and the reproducibility of building blocks. In this paper, we propose a new …

Evolutionary methods are well-known tools used for solving hard computational problems. In
this paper, we consider k-Shortest Steiner Trees (kSST) problem appearing in a diverse set …

The maintenance of many subpopulations is an important technique employed in
evolutionary methods. However, the use of a multi-population approach has its drawbacks …

This paper presents an evolutionary coding method that maps genotype to phenotype in a
genetic algorithm. Unlike traditional genetic algorithms, the proposed algorithm involves …

We present a novel representation and crossover operator for genetic algorithms. Bits are
not linked to one another. Instead, the current population suggests a pseudo-distance …

Many urban areas rely on bus transit to provide mobility to people. Transit priority strategies
can be used to improve bus operations. However, implementing transit priority applications …