Tutorial - Modern Linkage Learning Techniques in Combinatorial Optimization

Michal Przewozniczek, Department of Computer Science, Wroclaw University of Science and Technology, Poland ABSTRACT: Linkage learning is employed by many state-of-the-art evolutionary methods dedicated to solving problems in various domains: binary, discrete non-binary, permutation-based, continuous, and others. It has been successfully applied to solving single- and multi-objective problems. The information about underlying problem structure, discovered by linkage learning, is the key part of many state-of-the-art evolutionary methods. However, linkage learning techniques are often considered hard to understand or difficult to use. Linkage learning techniques apply to any optimization domain. However, linkage learning techniques dedicated to continuous search spaces are usually significantly different than those proposed for combinatorial problems. Therefore, this tutorial will focus on linkage learning techniques dedicated to discrete (including binary) and permutation-based search spaces. Nevertheless, for the presented techniques, we will point to their successful applications in continuous search space