Theoretical Foundations of Evolutionary Computation

in Special Issue   Posted on March 11, 2021 

Information for the Special Issue

Submission Deadline: Fri 31 Dec 2021
Journal Impact Factor : 0.747
Journal Name : Theoretical Computer Science
Journal Publisher:
Website for the Special Issue: https://www.journals.elsevier.com/theoretical-computer-science/call-for-papers/special-issue-on-theoretical-foundations-of-evolutionary-com
Journal & Submission Website: https://www.journals.elsevier.com/theoretical-computer-science

Special Issue Call for Papers:

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.

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.

Closed Special Issues