Evolutionary Computation is a leading journal in its field. It provides an international forum for facilitating and enhancing the exchange of information among researchers involved in both the theoretical and practical aspects of computational systems drawing their inspiration from nature, with particular emphasis on evolutionary models of computation such as genetic algorithms, evolutionary strategies, classifier systems, evolutionary programming, and genetic programming. It welcomes articles from related fields such as swarm intelligence (e.g. Ant Colony Optimization and Particle Swarm Optimization), and other nature-inspired computation paradigms (e.g. Artificial Immune Systems). As well as publishing articles describing theoretical and/or experimental work, the journal also welcomes application-focused papers describing breakthrough results in an application domain or methodological papers where the specificities of the real-world problem led to significant algorithmic improvements that could possibly be generalized to other areas.

Special Issue on 30th Anniversary of XCS
截稿日期: 2024-10-31

Guest Editors
● Anthony Stein, Tenure Track Professor of Artificial Intelligence in Agricultural
Engineering, University of Hohenheim, Germany
● Ryan Urbanowicz, Assistant Professor of Computational Biomedicine, Cedars-Sinai, Los
Angeles, CA
● Will Browne, Professor and Chair of Manufacturing Robotics, Queensland University of
Technology, Brisbane, Australia

Learning Classifier Systems (LCSs) are one of, if not, the first Evolutionary Computation
algorithms to adopt machine learning methods. Thus, they belong to the class of evolutionary
machine learning algorithms. With a rule-based model representation at their core, they possess
unique and valuable properties, such as inherent interpretability of learned solutions and the
ability to model extremely complex and heterogeneous relationships. LCSs were conceived in
the mid 1970s by evolutionary computation pioneer John Holland. At that time, these systems
were designed to model adaptive agents in his pursuit to understand complex adaptive systems.
Subsequently, LCSs have proven themselves to be a very effective, flexible, and broadly
applicable approach to predictive modeling and sequential problem solving tasks. They have
been successful not only in well-recognized benchmark tasks, e.g., exceeding previous limits in
solving multiplexer problems, but equally important, these systems often excel at solving
complex classification and regression problems in real-world domains such as biomedicine and
intelligent system control.

What is XCS?

XCS is the archetypal LCS as it embodies many core principles, whilst acting as a framework to
address bespoke problems. It belongs to the category of Michigan-style LCSs, one of the two
major families of LCSs algorithms. This style is characterized by adopting an online-learning
strategy, and employing steady-state niche genetic algorithms to optimize the coverage of the
problem spaces at hand. XCS differs from earlier Michigan-style LCSs by its accuracy-based
fitness, which has been shown to lead rule-discovery to explore a complete and maximally
compact learned problem solution. XCS is the extension of the Zeroth-level Classifier System,
both proposed and made popular by Stewart Wilson in the mid-1990’s [published in ECJ, hence
making it an ideal home for this special issue].

Since the inception of XCS, interest in LCSs experienced a new impetus and over three
decades of LCS research have been sparked, leading to outstanding advances of the system in
terms of algorithmic innovations, formal theoretical understanding, and a wide-range of
real-world applications. Even so, there remains enormous potential to expand and improve this
class of evolutionary machine learning systems. For example, while the deep learning era has
brought many innovations in the utilization of deep neural networks in almost all domains of
artificial intelligence, the integration of deep learning with LCSs has, to date, been limited to a
handful of promising works that has the potential to lead to a resurgence of interest. Currently,
there is a growing interest in neurosymbolic systems where the flexible structure of LCSs
provides a framework to integrate connectionist with symbolic learning.

Therefore, for this special issue we solicit papers that explore and contribute to the discussion
on open questions such as:
● How to fuse XCS-based systems with deep learning (or other cutting-edge algorithm)
concepts maintaining the idiosyncratic advantages of both: e.g., conducting flexible
online interpretable machine learning combined with the ability to efficiently and
accurately model extremely complex problems through hierarchical feature learning.
● What algorithmic and/or theoretical advances are still needed to overcome persisting
limitations of XCS, e.g., the maintenance of long action chains in delayed reward
settings within contemporary reinforcement learning tasks?
● What are novel or potentially untapped application domains, where XCS has been found
particularly advantageous over other machine learning techniques?
● What are the latest deep insights into XCS resulting from mathematical analysis, ablation
studies or rigorous method interaction analysis?

Article categories and submission instructions
We solicit manuscripts which belong to the following article types offered by ECJ:
● Full-length original research articles (including surveys, typically approx. 25 pages)
● Letters (short articles, typically approx. 6 pages)

ECJ accepts papers that broadly fall into the three categories: Applications, Experimental
Results and Theory. Of course, many papers may fall into more than one category.
The focus of this special issue is not exclusively set on original research papers, but
welcomes survey-type papers alike. In case of contributions concentrating on novel
applications, it must be thoroughly explained why XCS is particularly suited, what algorithmic
adaptations facilitate its adoption and how the presented XCS-based approach compares to
alternative methods.

Please carefully follow the general submission guidelines of the Evolutionary Computation
journal, which also apply to this special issue. Submissions are handled over the Evolutionary
Computation Editorial Manager. Authors must select "Special Issue: 30th Anniversary XCS" as
the article type when submitting.

Review and Process

All submissions will receive a minimum of two reviews, with at least one reviewer with a strong
LCS background and another reviewer with a more broader perspective on the EC and EML
field or, in case of manuscripts focussing on XCS’ application to new domains, one reviewer will
be selected from the specific application domain.

Please submit your manuscripts until October 31, 2024.

Anticipated timeline:
Manuscript submission: October 31, 2024
Author notification: April 15, 2025
Revision phase until: September 2025
Finalization: October 2025

We invite prospective authors who plan to contribute a survey-type paper to inform the guest
editor team upfront in order to prevent potential duplications of efforts. In case of any questions,
don’t hesitate to write an email to: anthony.stein@uni-hohenheim.de