Swarm and Evolutionary Computation (JCR 6.330, Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE) ISSN: 2210-6502 Elsevier |
Overview
For at least the last three decades of the field of Evolutionary Computing, a growing number of researchers have focused their efforts on combining different methods and functionalities into a single solver. In general, the aim was to overcome disadvantages of some individual solvers and/or to improve the performance rendered by off-the-shelf optimization methods. In this regard, Memetic Algorithms (MA) spearhead this design principle by exploiting the synergies of individual search procedures in evolutionary optimization frameworks leading to development of the Memetic Computing (MC) field. Since its inception by Moscato and Norman in late ’80s, MC has blossomed into a manifold of algorithmic variants, to yield one of the most prolific areas within Swarm Intelligence and Evolutionary Computation to date. Indeed, MC have been growing fast to yield complex techniques with extremely sophisticated exploitation and cooperation mechanisms. A variety of MAs continue to use Evolutionary/Bio-inspired/Swarm Intelligence approaches for global optimization (both combinatorial and non-linear or mixed) with separate individual improvement and adaptive or learning mechanisms, generally incorporating domain-specific knowledge for the problem under analysis.
Scope
This special issue aims at disseminating the latest findings and research achievements in MAs, with a special attention paid to contributions focused on problem-dependent individual/local search methods and solutions. We also welcome theoretical research ideas and their application to real-world problems. To this end, we solicit high-quality original submissions to this special issue that reflect the unprecedented momentum garnered by this research area.
Topics
Topics of interest include, but are not limited to:
- Recent advances on the combination of population-based global optimization solvers with problem-dependent local search procedures.
- Real-world applications of Memetic Computation and Memetic Algorithms.
- Evidences of the applicability of Memetic Algorithms and Memetic Computing to emerging paradigms such as Large-Scale Global Optimization, Transfer Optimization or Neuroevolution.
- Novel insights of Memetic Computing applied to multi- and many-objective optimization.
- Memetic Algorithms for symbolic regression and time-series prediction.
- Complete Anytime Memetic Algorithms (MAs that can deliver feasible solutions if stopped but that will stop by themselves if they have found the optimal solution).
- New findings on memetic transmission, design selection and design patterns.
- Advances on co-evolving methods and self-adaptive memetic schemes.
- Theoretical and practical studies exploring the balance between exploration and exploitation in MAs.
- New procedures for detecting and quantifying the level of stagnation on MAs, and novel trends for enhancing diversification.
Submission
Submitted papers should be original and are not be under consideration elsewhere for publication. Prospective authors should follow the journal guidelines, regarding the manuscript content and its format when preparing their manuscripts. All papers will be reviewed by at least three independent reviewers for their suitability in terms of technical novelty, scientific rigor, scope, and relevance to this special issue. When submitting papers, please select Article Type “VSI: Memetic Computing”
Important Dates
- June 1, 2020: Call for papers.
- August 1, 2020: Deadline for Initial Paper Submission.
- November 1, 2020: Notification of First Round Decision.
- December 15, 2020: Deadline for Revised Paper Submission.
- February 15, 2021: Final acceptance decision.
- June 1, 2021: Target publication date.
Guest Editors
- Dr. Eneko Osaba Icedo (eneko.osaba(at)tecnalia.com), TECNALIA, Spain
- Prof. Dr. Javier Del Ser (javier.delser(at)tecnalia.com), TECNALIA, Spain, and University of the Basque Country (UPV/EHU), Spain
- Prof. Dr. Carlos Cotta (ccottap(at)lcc.uma.es), Universidad de Malaga, Spain
- Prof. Dr. Pablo Moscato (Pablo.moscato(at)newcastle.edu.au), The University of Newcastle, Callaghan, Australia