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Tutorial
on Modern Linkage Learning Techniques in Combinatorial Optimization |
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The 2022 year updates in this
tutorial are marked in red. |
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Aim
and scope of the tutorial |
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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. Therefore, the
aims of this tutorial are as follows: -
Present the current research state
concerning linkage learning (more attention paid to
the recent propositions concerning Empirical Linkage Learning, including the
Dark Gray-box optimization) -
Present in detail, using
easy-to-understand examples, the work of modern linkage learning techniques -
Present in detail, using
easy-to-understand examples, how linkage may be represented and utilized -
Present the details of state-of-the-art
evolutionary methods that employ linkage learning -
Present the differences between linkage
learning application in single- and multi-objective optimization problems -
Present how linkage quality affects the
evolutionary search -
Present the most important challenges that
are currently faced by linkage learning development -
Present the most promising future work directions
for research that concerns linkage learning 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 spaces. The main utility
features of the tutorial will be as follows. -
All
parts of the tutorial will be based on simple examples that will picture the
presented issues -
The
main aim of the tutorial is to make the issues related to modern linkage
learning easy-to-understand For the tutorial we
will prepare a source code pack
(accessible via GitHub), so the participants will be able to test the
discussed techniques and methods on their own |
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Tutorial
length |
2 hours |
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Tutorial
level |
introductory |
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