[DMANET] CFP: Special Issue on Theoretical Foundations of Evolutionary Computation

Dear All,

We invite submissions of papers on the theory of evolutionary
computation for publication in a special issue of Theoretical Computer
Science, Track C -Theory of Natural Computing.

=====================================
CALL FOR PAPERS

Theoretical Computer Science Special Issue on
Theoretical Foundations of Evolutionary Computation
https://www.journals.elsevier.com/theoretical-computer-science/call-for-papers/special-issue-on-theoretical-foundations-of-evolutionary-com
Submission deadline: December 31, 2021
=====================================

Guest Editors

Per Kristian Lehre, University of Birmingham, U.K.
Aneta Neumann, The University of Adelaide, Australia
Chao Qian, Nanjing University, China

Aim

Evolutionary computation methods such as evolutionary algorithms and
swarm intelligence algorithms have been successfully applied to a wide
range of real-world optimization problems. Conventional optimization
algorithms often require objective functions to be differentiable,
continuous, or convex. However, these assumptions are often not met in
real-world optimization. In contrast, evolutionary computation methods
only assume (zeroth order) black-box access to the objective values of
solutions.

Mimicking natural phenomena, evolutionary computation methods are often
highly randomized and complex, making a theoretical analysis
challenging. During the past two decades, there have been significant
theoretical efforts. This research has brought general theoretical
analysis tools such as fitness levels and drift analysis. These results
provide useful insights into the working principles of evolutionary
computation methods which have helped practitioners design more powerful
algorithms. As evolutionary computation methods are applied to more
complex real-world optimization problems, there is a need to further
understand these methods theoretically. Thus, this special issue aims to
advance the theoretical understanding of evolutionary computation
methods.

Scope

This special issue solicits original, high-quality contributions on the
theory of evolutionary computation. The scope includes, but is not
limited to:

General analytical methods like fitness levels and drift analysis
Exact and approximation runtime analysis
Black-box complexity
Population diversity and dynamics
Variation and selection operators
Self-adaptation
Fitness landscape and problem difficulty analysis

All classes of evolutionary computation methods will be considered
including (but not limited to): evolutionary algorithms, ant colony
optimization, artificial immune systems, particle swarm optimization,
estimation of distribution algorithms and differential evolution. All
problem domains will be considered, including (but not limited to):
discrete and continuous optimization, single-objective and
multi-objective optimization, constrained optimization, and optimization
under uncertainty (e.g., noisy, dynamic and robust optimization).

Planned Schedule

Submission deadline: December 31, 2021
Notification: September 31, 2022
Final paper submission: October 31, 2022
Tentative publication date: 2023

Submission Instructions

Authors should submit their manuscripts to the Theoretical Computer
Science Editorial System (EM) at
https://www.editorialmanager.com/tcs/default.aspx, and indicate "Special
Issue: Theoretical Foundations of Evolutionary Computation" for their
submission.

Contributions should be typeset in PDF format or the system converts
article files to a single PDF file used in the peer-review
process. Editable files (e.g., Word, LaTeX) are required to typeset an
article for final publication, and must comply with TCS's author
guidelines, which can be retrieved from the Elsevier website,
https://www.elsevier.com/journals/theoretical-computer-science/0304-3975/guide-for-authors.

Please feel free to forward this call for submissions to your colleagues/other relevant mailing lists,
and do not hesitate to contact us if you have any questions.
We look forward to your contributions.

Best wishes,
Per Kristian Lehre, Chao Qian and Aneta Neumann






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