Saturday, 17 March 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 subareas
of the theory of bio-inspired algorithms. The scope of
this special issue includes (but is not limited to) the following

• 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

For further information, please contact one of the following
Guest Editors.


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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