Monday 17 September 2018

CFP: IEEE TEVC Special Issue on Theoretical Foundations of Evolutionary Computation (Oct 1)


  Evolutionary computation (EC) methods such as evolutionary algorithms, ant colony optimization and artificial immune systems have been successfully applied to a wide range of problems. These include classical combinatorial optimization problems and a variety of continuous, discrete and mixed integer real-world optimization problems that are often hard to optimize by traditional methods (e.g., because they are non-linear, highly constrained, multi-objective, etc.). In contrast to the successful applications, there is still a need to understand the behaviour of these algorithms. The achievement and development of a solid theory of bio-inspired computation techniques is important as it provides sound knowledge on their working principles. In particular, it explains the success or the failure of these methods in practical applications. Theoretical analyses lead to the understanding of which problems are optimized (or approximated) efficiently by a given algorithm and which ones are not. The benefits of theoretical understanding for practitioners are threefold. 1) Aiding algorithm design, 2) guiding the choice of the best algorithm for the problem at hand and 3) determining optimal parameter settings.
  The aim of this special issue is to advance the theoretical understanding of evolutionary computation methods. We solicit novel, high quality scientific contributions on theoretical or foundational aspects of evolutionary computation. A successful exchange between theory and practice in evolutionary computation is very desirable and papers bridging theory and practice are of particular interest. In addition to strict mathematical investigations, experimental studies strengthening the theoretical foundations of evolutionary computation methods are very welcome.


  This special issue will present novel results from different sub- areas of the theory of bio-inspired algorithms. The scope of this special issue includes (but is not limited to) the following topics:
  • Exact and approximation runtime analysis
  • Black box complexity
  • Self-adaptation
  • Population dynamics
  • Fitness landscape and problem difficulty analysis
  • No free lunch theorems
  • Theoretical Foundations of combining traditional optimization techniques with EC methods
  • Statistical approaches for understanding the behaviour of bio-inspired heuristics
  • Computational studies of a foundational nature
  All classes of bio-inspired optimization algorithms will be considered including (but not limited to) evolutionary algorithms, ant colony optimization, artificial immune systems, particle swarm optimization, differential evolution, and estimation of distribution algorithms. All problem domains will be considered including discrete and continuous optimization, single-objective and multi-objective optimization, constraint handling, dynamic and stochastic optimization, co-evolution and evolutionary learning.


Manuscripts should be prepared according to the “Information for Authors” section of the journal found at and submissions should be made through the journal submission website:, by selecting the Manuscript Type of “TFoEC Special Issue Papers” and clearly adding “TFoEC Special Issue Paper” to the comments to the Editor-in-Chief.

Submitted papers will be reviewed by at least three different expert reviewers. Submission of a manuscript implies that it is the authors’ original unpublished work and is not being submitted for possible publication elsewhere.

Each submission will contain at least one paragraph explaining why the paper is (potentially) relevant to practice.


Submission open: February 1, 2018
Submission deadline: October 1, 2018
Tentative publication date: 2019

Papers will be assigned to reviewers as soon as they are submitted. Papers will be published online as soon as they are accepted.

For further information, please contact one of the following GuestEditors.


Pietro S. Oliveto

Department of Computer Science
University of Sheffield
United Kingdom

Anne Auger

Ecole Polytechnique Paris

Francisco Chicano

Department of Languages and Computing Sciences
University of Malaga

Carlos M. Fonseca

Department of Informatics Engineering
University of Coimbra

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